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29,067 | em | We present a class of one-to-one matching models with perfectly transferable
utility. We discuss identification and inference in these separable models, and
we show how their comparative statics are readily analyzed. | The Econometrics and Some Properties of Separable Matching Models | 2021-02-04 14:55:10 | Alfred Galichon, Bernard Salanié | http://dx.doi.org/10.1257/aer.p20171113, http://arxiv.org/abs/2102.02564v1, http://arxiv.org/pdf/2102.02564v1 | econ.EM |
29,068 | em | The notion of hypothetical bias (HB) constitutes, arguably, the most
fundamental issue in relation to the use of hypothetical survey methods.
Whether or to what extent choices of survey participants and subsequent
inferred estimates translate to real-world settings continues to be debated.
While HB has been extensively studied in the broader context of contingent
valuation, it is much less understood in relation to choice experiments (CE).
This paper reviews the empirical evidence for HB in CE in various fields of
applied economics and presents an integrative framework for how HB relates to
external validity. Results suggest mixed evidence on the prevalence, extent and
direction of HB as well as considerable context and measurement dependency.
While HB is found to be an undeniable issue when conducting CEs, the empirical
evidence on HB does not render CEs unable to represent real-world preferences.
While health-related choice experiments often find negligible degrees of HB,
experiments in consumer behaviour and transport domains suggest that
significant degrees of HB are ubiquitous. Assessments of bias in environmental
valuation studies provide mixed evidence. Also, across these disciplines many
studies display HB in their total willingness to pay estimates and opt-in rates
but not in their hypothetical marginal rates of substitution (subject to scale
correction). Further, recent findings in psychology and brain imaging studies
suggest neurocognitive mechanisms underlying HB that may explain some of the
discrepancies and unexpected findings in the mainstream CE literature. The
review also observes how the variety of operational definitions of HB prohibits
consistent measurement of HB in CE. The paper further identifies major sources
of HB and possible moderating factors. Finally, it explains how HB represents
one component of the wider concept of external validity. | Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity | 2021-02-05 03:45:50 | Milad Haghani, Michiel C. J. Bliemer, John M. Rose, Harmen Oppewal, Emily Lancsar | http://dx.doi.org/10.1016/j.jocm.2021.100309, http://arxiv.org/abs/2102.02940v1, http://arxiv.org/pdf/2102.02940v1 | econ.EM |
29,069 | em | This paper reviews methods of hypothetical bias (HB) mitigation in choice
experiments (CEs). It presents a bibliometric analysis and summary of empirical
evidence of their effectiveness. The paper follows the review of empirical
evidence on the existence of HB presented in Part I of this study. While the
number of CE studies has rapidly increased since 2010, the critical issue of HB
has been studied in only a small fraction of CE studies. The present review
includes both ex-ante and ex-post bias mitigation methods. Ex-ante bias
mitigation methods include cheap talk, real talk, consequentiality scripts,
solemn oath scripts, opt-out reminders, budget reminders, honesty priming,
induced truth telling, indirect questioning, time to think and pivot designs.
Ex-post methods include follow-up certainty calibration scales, respondent
perceived consequentiality scales, and revealed-preference-assisted estimation.
It is observed that the use of mitigation methods markedly varies across
different sectors of applied economics. The existing empirical evidence points
to their overall effectives in reducing HB, although there is some variation.
The paper further discusses how each mitigation method can counter a certain
subset of HB sources. Considering the prevalence of HB in CEs and the
effectiveness of bias mitigation methods, it is recommended that implementation
of at least one bias mitigation method (or a suitable combination where
possible) becomes standard practice in conducting CEs. Mitigation method(s)
suited to the particular application should be implemented to ensure that
inferences and subsequent policy decisions are as much as possible free of HB. | Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods | 2021-02-05 03:53:21 | Milad Haghani, Michiel C. J. Bliemer, John M. Rose, Harmen Oppewal, Emily Lancsar | http://dx.doi.org/10.1016/j.jocm.2021.100322, http://arxiv.org/abs/2102.02945v1, http://arxiv.org/pdf/2102.02945v1 | econ.EM |
29,070 | em | We provide a geometric formulation of the problem of identification of the
matching surplus function and we show how the estimation problem can be solved
by the introduction of a generalized entropy function over the set of
matchings. | Identification of Matching Complementarities: A Geometric Viewpoint | 2021-02-07 21:31:54 | Alfred Galichon | http://dx.doi.org/10.1108/S0731-9053(2013)0000032005, http://arxiv.org/abs/2102.03875v1, http://arxiv.org/pdf/2102.03875v1 | econ.EM |
29,071 | em | This paper studies inference in a randomized controlled trial (RCT) with
covariate-adaptive randomization (CAR) and imperfect compliance of a binary
treatment. In this context, we study inference on the LATE. As in Bugni et al.
(2018,2019), CAR refers to randomization schemes that first stratify according
to baseline covariates and then assign treatment status so as to achieve
"balance" within each stratum. In contrast to these papers, however, we allow
participants of the RCT to endogenously decide to comply or not with the
assigned treatment status.
We study the properties of an estimator of the LATE derived from a "fully
saturated" IV linear regression, i.e., a linear regression of the outcome on
all indicators for all strata and their interaction with the treatment
decision, with the latter instrumented with the treatment assignment. We show
that the proposed LATE estimator is asymptotically normal, and we characterize
its asymptotic variance in terms of primitives of the problem. We provide
consistent estimators of the standard errors and asymptotically exact
hypothesis tests. In the special case when the target proportion of units
assigned to each treatment does not vary across strata, we can also consider
two other estimators of the LATE, including the one based on the "strata fixed
effects" IV linear regression, i.e., a linear regression of the outcome on
indicators for all strata and the treatment decision, with the latter
instrumented with the treatment assignment.
Our characterization of the asymptotic variance of the LATE estimators allows
us to understand the influence of the parameters of the RCT. We use this to
propose strategies to minimize their asymptotic variance in a hypothetical RCT
based on data from a pilot study. We illustrate the practical relevance of
these results using a simulation study and an empirical application based on
Dupas et al. (2018). | Inference under Covariate-Adaptive Randomization with Imperfect Compliance | 2021-02-08 01:36:26 | Federico A. Bugni, Mengsi Gao | http://arxiv.org/abs/2102.03937v3, http://arxiv.org/pdf/2102.03937v3 | econ.EM |
29,079 | em | Using results from convex analysis, we investigate a novel approach to
identification and estimation of discrete choice models which we call the Mass
Transport Approach (MTA). We show that the conditional choice probabilities and
the choice-specific payoffs in these models are related in the sense of
conjugate duality, and that the identification problem is a mass transport
problem. Based on this, we propose a new two-step estimator for these models;
interestingly, the first step of our estimator involves solving a linear
program which is identical to the classic assignment (two-sided matching) game
of Shapley and Shubik (1971). The application of convex-analytic tools to
dynamic discrete choice models, and the connection with two-sided matching
models, is new in the literature. | Duality in dynamic discrete-choice models | 2021-02-08 18:50:03 | Khai Xiang Chiong, Alfred Galichon, Matt Shum | http://dx.doi.org/10.3982/QE436, http://arxiv.org/abs/2102.06076v2, http://arxiv.org/pdf/2102.06076v2 | econ.EM |
29,072 | em | In a landmark contribution to the structural vector autoregression (SVARs)
literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector
Autoregressions: Theory of Identification and Algorithms for Inference,' Review
of Economic Studies) shows a necessary and sufficient condition for equality
restrictions to globally identify the structural parameters of a SVAR. The
simplest form of the necessary and sufficient condition shown in Theorem 7 of
Rubio-Ramirez et al (2010) checks the number of zero restrictions and the ranks
of particular matrices without requiring knowledge of the true value of the
structural or reduced-form parameters. However, this note shows by
counterexample that this condition is not sufficient for global identification.
Analytical investigation of the counterexample clarifies why their sufficiency
claim breaks down. The problem with the rank condition is that it allows for
the possibility that restrictions are redundant, in the sense that one or more
restrictions may be implied by other restrictions, in which case the implied
restriction contains no identifying information. We derive a modified necessary
and sufficient condition for SVAR global identification and clarify how it can
be assessed in practice. | A note on global identification in structural vector autoregressions | 2021-02-08 11:14:27 | Emanuele Bacchiocchi, Toru Kitagawa | http://arxiv.org/abs/2102.04048v2, http://arxiv.org/pdf/2102.04048v2 | econ.EM |
29,073 | em | We propose an easily implementable test of the validity of a set of
theoretical restrictions on the relationship between economic variables, which
do not necessarily identify the data generating process. The restrictions can
be derived from any model of interactions, allowing censoring and multiple
equilibria. When the restrictions are parameterized, the test can be inverted
to yield confidence regions for partially identified parameters, thereby
complementing other proposals, primarily Chernozhukov et al. [Chernozhukov, V.,
Hong, H., Tamer, E., 2007. Estimation and confidence regions for parameter sets
in econometric models. Econometrica 75, 1243-1285]. | A test of non-identifying restrictions and confidence regions for partially identified parameters | 2021-02-08 15:01:13 | Alfred Galichon, Marc Henry | http://dx.doi.org/10.1016/j.jeconom.2009.01.010, http://arxiv.org/abs/2102.04151v1, http://arxiv.org/pdf/2102.04151v1 | econ.EM |
29,074 | em | A general framework is given to analyze the falsifiability of economic models
based on a sample of their observable components. It is shown that, when the
restrictions implied by the economic theory are insufficient to identify the
unknown quantities of the structure, the duality of optimal transportation with
zero-one cost function delivers interpretable and operational formulations of
the hypothesis of specification correctness from which tests can be constructed
to falsify the model. | Optimal transportation and the falsifiability of incompletely specified economic models | 2021-02-08 15:25:46 | Ivar Ekeland, Alfred Galichon, Marc Henry | http://dx.doi.org/10.1007/s00199-008-0432-y, http://arxiv.org/abs/2102.04162v2, http://arxiv.org/pdf/2102.04162v2 | econ.EM |
29,075 | em | Despite their popularity, machine learning predictions are sensitive to
potential unobserved predictors. This paper proposes a general algorithm that
assesses how the omission of an unobserved variable with high explanatory power
could affect the predictions of the model. Moreover, the algorithm extends the
usage of machine learning from pointwise predictions to inference and
sensitivity analysis. In the application, we show how the framework can be
applied to data with inherent uncertainty, such as students' scores in a
standardized assessment on financial literacy. First, using Bayesian Additive
Regression Trees (BART), we predict students' financial literacy scores (FLS)
for a subgroup of students with missing FLS. Then, we assess the sensitivity of
predictions by comparing the predictions and performance of models with and
without a highly explanatory synthetic predictor. We find no significant
difference in the predictions and performances of the augmented (i.e., the
model with the synthetic predictor) and original model. This evidence sheds a
light on the stability of the predictive model used in the application. The
proposed methodology can be used, above and beyond our motivating empirical
example, in a wide range of machine learning applications in social and health
sciences. | Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy | 2021-02-08 20:42:10 | Falco J. Bargagli Stoffi, Kenneth De Beckker, Joana E. Maldonado, Kristof De Witte | http://arxiv.org/abs/2102.04382v1, http://arxiv.org/pdf/2102.04382v1 | econ.EM |
29,076 | em | We propose a methodology for constructing confidence regions with partially
identified models of general form. The region is obtained by inverting a test
of internal consistency of the econometric structure. We develop a dilation
bootstrap methodology to deal with sampling uncertainty without reference to
the hypothesized economic structure. It requires bootstrapping the quantile
process for univariate data and a novel generalization of the latter to higher
dimensions. Once the dilation is chosen to control the confidence level, the
unknown true distribution of the observed data can be replaced by the known
empirical distribution and confidence regions can then be obtained as in
Galichon and Henry (2011) and Beresteanu, Molchanov and Molinari (2011). | Dilation bootstrap | 2021-02-08 17:13:37 | Alfred Galichon, Marc Henry | http://dx.doi.org/10.1016/j.jeconom.2013.07.001, http://arxiv.org/abs/2102.04457v1, http://arxiv.org/pdf/2102.04457v1 | econ.EM |
29,077 | em | This article proposes a generalized notion of extreme multivariate dependence
between two random vectors which relies on the extremality of the
cross-covariance matrix between these two vectors. Using a partial ordering on
the cross-covariance matrices, we also generalize the notion of positive upper
dependence. We then proposes a means to quantify the strength of the dependence
between two given multivariate series and to increase this strength while
preserving the marginal distributions. This allows for the design of
stress-tests of the dependence between two sets of financial variables, that
can be useful in portfolio management or derivatives pricing. | Extreme dependence for multivariate data | 2021-02-08 17:57:13 | Damien Bosc, Alfred Galichon | http://dx.doi.org/10.1080/14697688.2014.886777, http://arxiv.org/abs/2102.04461v1, http://arxiv.org/pdf/2102.04461v1 | econ.EM |
29,078 | em | Responding to the U.S. opioid crisis requires a holistic approach supported
by evidence from linking and analyzing multiple data sources. This paper
discusses how 20 available resources can be combined to answer pressing public
health questions related to the crisis. It presents a network view based on
U.S. geographical units and other standard concepts, crosswalked to communicate
the coverage and interlinkage of these resources. These opioid-related datasets
can be grouped by four themes: (1) drug prescriptions, (2) opioid related
harms, (3) opioid treatment workforce, jobs, and training, and (4) drug policy.
An interactive network visualization was created and is freely available
online; it lets users explore key metadata, relevant scholarly works, and data
interlinkages in support of informed decision making through data analysis. | Interactive Network Visualization of Opioid Crisis Related Data- Policy, Pharmaceutical, Training, and More | 2021-02-10 20:51:48 | Olga Scrivner, Elizabeth McAvoy, Thuy Nguyen, Tenzin Choeden, Kosali Simon, Katy Börner | http://arxiv.org/abs/2102.05596v1, http://arxiv.org/pdf/2102.05596v1 | econ.EM |
29,081 | em | We consider structural vector autoregressions subject to 'narrative
restrictions', which are inequality restrictions on functions of the structural
shocks in specific periods. These restrictions raise novel problems related to
identification and inference, and there is currently no frequentist procedure
for conducting inference in these models. We propose a solution that is valid
from both Bayesian and frequentist perspectives by: 1) formalizing the
identification problem under narrative restrictions; 2) correcting a feature of
the existing (single-prior) Bayesian approach that can distort inference; 3)
proposing a robust (multiple-prior) Bayesian approach that is useful for
assessing and eliminating the posterior sensitivity that arises in these models
due to the likelihood having flat regions; and 4) showing that the robust
Bayesian approach has asymptotic frequentist validity. We illustrate our
methods by estimating the effects of US monetary policy under a variety of
narrative restrictions. | Identification and Inference Under Narrative Restrictions | 2021-02-12 14:38:55 | Raffaella Giacomini, Toru Kitagawa, Matthew Read | http://arxiv.org/abs/2102.06456v1, http://arxiv.org/pdf/2102.06456v1 | econ.EM |
29,082 | em | Weak instruments present a major setback to empirical work. This paper
introduces an estimator that admits weak, uncorrelated, or mean-independent
instruments that are non-independent of endogenous covariates. Relative to
conventional instrumental variable methods, the proposed estimator weakens the
relevance condition considerably without imposing a stronger exclusion
restriction. Identification mainly rests on (1) a weak conditional median
exclusion restriction imposed on pairwise differences in disturbances and (2)
non-independence between covariates and instruments. Under mild conditions, the
estimator is consistent and asymptotically normal. Monte Carlo experiments
showcase an excellent performance of the estimator, and two empirical examples
illustrate its practical utility. | A Distance Covariance-based Estimator | 2021-02-14 00:55:09 | Emmanuel Selorm Tsyawo, Abdul-Nasah Soale | http://arxiv.org/abs/2102.07008v1, http://arxiv.org/pdf/2102.07008v1 | econ.EM |
29,083 | em | This paper contributes to the literature on hedonic models in two ways.
First, it makes use of Queyranne's reformulation of a hedonic model in the
discrete case as a network flow problem in order to provide a proof of
existence and integrality of a hedonic equilibrium and efficient computation of
hedonic prices. Second, elaborating on entropic methods developed in Galichon
and Salani\'{e} (2014), this paper proposes a new identification strategy for
hedonic models in a single market. This methodology allows one to introduce
heterogeneities in both consumers' and producers' attributes and to recover
producers' profits and consumers' utilities based on the observation of
production and consumption patterns and the set of hedonic prices. | Entropy methods for identifying hedonic models | 2021-02-15 14:49:21 | Arnaud Dupuy, Alfred Galichon, Marc Henry | http://dx.doi.org/10.1007/s11579-014-0125-1, http://arxiv.org/abs/2102.07491v1, http://arxiv.org/pdf/2102.07491v1 | econ.EM |
29,084 | em | Unlike other techniques of causality inference, the use of valid instrumental
variables can deal with unobserved sources of both variable errors, variable
omissions, and sampling bias, and still arrive at consistent estimates of
average treatment effects. The only problem is to find the valid instruments.
Using the definition of Pearl (2009) of valid instrumental variables, a formal
condition for validity can be stated for variables in generalized linear causal
models. The condition can be applied in two different ways: As a tool for
constructing valid instruments, or as a foundation for testing whether an
instrument is valid. When perfectly valid instruments are not found, the
squared bias of the IV-estimator induced by an imperfectly valid instrument --
estimated with bootstrapping -- can be added to its empirical variance in a
mean-square-error-like reliability measure. | Constructing valid instrumental variables in generalized linear causal models from directed acyclic graphs | 2021-02-16 13:09:15 | Øyvind Hoveid | http://arxiv.org/abs/2102.08056v1, http://arxiv.org/pdf/2102.08056v1 | econ.EM |
29,085 | em | We propose a general framework for the specification testing of continuous
treatment effect models. We assume a general residual function, which includes
the average and quantile treatment effect models as special cases. The null
models are identified under the unconfoundedness condition and contain a
nonparametric weighting function. We propose a test statistic for the null
model in which the weighting function is estimated by solving an expanding set
of moment equations. We establish the asymptotic distributions of our test
statistic under the null hypothesis and under fixed and local alternatives. The
proposed test statistic is shown to be more efficient than that constructed
from the true weighting function and can detect local alternatives deviated
from the null models at the rate of $O(N^{-1/2})$. A simulation method is
provided to approximate the null distribution of the test statistic.
Monte-Carlo simulations show that our test exhibits a satisfactory
finite-sample performance, and an application shows its practical value. | A Unified Framework for Specification Tests of Continuous Treatment Effect Models | 2021-02-16 13:18:52 | Wei Huang, Oliver Linton, Zheng Zhang | http://arxiv.org/abs/2102.08063v2, http://arxiv.org/pdf/2102.08063v2 | econ.EM |
29,086 | em | In light of the increasing interest to transform the fixed-route public
transit (FRT) services into on-demand transit (ODT) services, there exists a
strong need for a comprehensive evaluation of the effects of this shift on the
users. Such an analysis can help the municipalities and service providers to
design and operate more convenient, attractive, and sustainable transit
solutions. To understand the user preferences, we developed three hybrid choice
models: integrated choice and latent variable (ICLV), latent class (LC), and
latent class integrated choice and latent variable (LC-ICLV) models. We used
these models to analyze the public transit user's preferences in Belleville,
Ontario, Canada. Hybrid choice models were estimated using a rich dataset that
combined the actual level of service attributes obtained from Belleville's ODT
service and self-reported usage behaviour obtained from a revealed preference
survey of the ODT users. The latent class models divided the users into two
groups with different travel behaviour and preferences. The results showed that
the captive user's preference for ODT service was significantly affected by the
number of unassigned trips, in-vehicle time, and main travel mode before the
ODT service started. On the other hand, the non-captive user's service
preference was significantly affected by the Time Sensitivity and the Online
Service Satisfaction latent variables, as well as the performance of the ODT
service and trip purpose. This study attaches importance to improving the
reliability and performance of the ODT service and outlines directions for
reducing operational costs by updating the required fleet size and assigning
more vehicles for work-related trips. | On-Demand Transit User Preference Analysis using Hybrid Choice Models | 2021-02-16 19:27:50 | Nael Alsaleh, Bilal Farooq, Yixue Zhang, Steven Farber | http://arxiv.org/abs/2102.08256v2, http://arxiv.org/pdf/2102.08256v2 | econ.EM |
29,087 | em | This article discusses tests for nonlinear cointegration in the presence of
variance breaks. We build on cointegration test approaches under
heteroskedasticity (Cavaliere and Taylor, 2006, Journal of Time Series
Analysis) and for nonlinearity (Choi and Saikkonen, 2010, Econometric Theory)
to propose a bootstrap test and prove its consistency. A Monte Carlo study
shows the approach to have good finite sample properties. We provide an
empirical application to the environmental Kuznets curves (EKC), finding that
the cointegration test provides little evidence for the EKC hypothesis.
Additionally, we examine the nonlinear relation between the US money and the
interest rate, finding that our test does not reject the null of a smooth
transition cointegrating relation. | Testing for Nonlinear Cointegration under Heteroskedasticity | 2021-02-17 18:14:19 | Christoph Hanck, Till Massing | http://arxiv.org/abs/2102.08809v2, http://arxiv.org/pdf/2102.08809v2 | econ.EM |
29,088 | em | This paper provides a user's guide to the general theory of approximate
randomization tests developed in Canay, Romano, and Shaikh (2017) when
specialized to linear regressions with clustered data. An important feature of
the methodology is that it applies to settings in which the number of clusters
is small -- even as small as five. We provide a step-by-step algorithmic
description of how to implement the test and construct confidence intervals for
the parameter of interest. In doing so, we additionally present three novel
results concerning the methodology: we show that the method admits an
equivalent implementation based on weighted scores; we show the test and
confidence intervals are invariant to whether the test statistic is studentized
or not; and we prove convexity of the confidence intervals for scalar
parameters. We also articulate the main requirements underlying the test,
emphasizing in particular common pitfalls that researchers may encounter.
Finally, we illustrate the use of the methodology with two applications that
further illuminate these points. The companion {\tt R} and {\tt Stata} packages
facilitate the implementation of the methodology and the replication of the
empirical exercises. | On the implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters | 2021-02-18 01:32:52 | Yong Cai, Ivan A. Canay, Deborah Kim, Azeem M. Shaikh | http://arxiv.org/abs/2102.09058v4, http://arxiv.org/pdf/2102.09058v4 | econ.EM |
29,089 | em | We propose a novel structural estimation framework in which we train a
surrogate of an economic model with deep neural networks. Our methodology
alleviates the curse of dimensionality and speeds up the evaluation and
parameter estimation by orders of magnitudes, which significantly enhances
one's ability to conduct analyses that require frequent parameter
re-estimation. As an empirical application, we compare two popular option
pricing models (the Heston and the Bates model with double-exponential jumps)
against a non-parametric random forest model. We document that: a) the Bates
model produces better out-of-sample pricing on average, but both structural
models fail to outperform random forest for large areas of the volatility
surface; b) random forest is more competitive at short horizons (e.g., 1-day),
for short-dated options (with less than 7 days to maturity), and on days with
poor liquidity; c) both structural models outperform random forest in
out-of-sample delta hedging; d) the Heston model's relative performance has
deteriorated significantly after the 2008 financial crisis. | Deep Structural Estimation: With an Application to Option Pricing | 2021-02-18 11:15:47 | Hui Chen, Antoine Didisheim, Simon Scheidegger | http://arxiv.org/abs/2102.09209v1, http://arxiv.org/pdf/2102.09209v1 | econ.EM |
29,090 | em | We propose a method for constructing confidence intervals that account for
many forms of spatial correlation. The interval has the familiar `estimator
plus and minus a standard error times a critical value' form, but we propose
new methods for constructing the standard error and the critical value. The
standard error is constructed using population principal components from a
given `worst-case' spatial covariance model. The critical value is chosen to
ensure coverage in a benchmark parametric model for the spatial correlations.
The method is shown to control coverage in large samples whenever the spatial
correlation is weak, i.e., with average pairwise correlations that vanish as
the sample size gets large. We also provide results on correct coverage in a
restricted but nonparametric class of strong spatial correlations, as well as
on the efficiency of the method. In a design calibrated to match economic
activity in U.S. states the method outperforms previous suggestions for
spatially robust inference about the population mean. | Spatial Correlation Robust Inference | 2021-02-18 17:04:43 | Ulrich K. Müller, Mark W. Watson | http://arxiv.org/abs/2102.09353v1, http://arxiv.org/pdf/2102.09353v1 | econ.EM |
29,091 | em | This paper aims to provide reliable estimates for the COVID-19 contact rate
of a Susceptible-Infected-Recovered (SIR) model. From observable data on
confirmed, recovered, and deceased cases, a noisy measurement for the contact
rate can be constructed. To filter out measurement errors and seasonality, a
novel unobserved components (UC) model is set up. It specifies the log contact
rate as a latent, fractionally integrated process of unknown integration order.
The fractional specification reflects key characteristics of aggregate social
behavior such as strong persistence and gradual adjustments to new information.
A computationally simple modification of the Kalman filter is introduced and is
termed the fractional filter. It allows to estimate UC models with richer
long-run dynamics, and provides a closed-form expression for the prediction
error of UC models. Based on the latter, a conditional-sum-of-squares (CSS)
estimator for the model parameters is set up that is shown to be consistent and
asymptotically normally distributed. The resulting contact rate estimates for
several countries are well in line with the chronology of the pandemic, and
allow to identify different contact regimes generated by policy interventions.
As the fractional filter is shown to provide precise contact rate estimates at
the end of the sample, it bears great potential for monitoring the pandemic in
real time. | Monitoring the pandemic: A fractional filter for the COVID-19 contact rate | 2021-02-19 20:55:45 | Tobias Hartl | http://arxiv.org/abs/2102.10067v1, http://arxiv.org/pdf/2102.10067v1 | econ.EM |
29,092 | em | A novel approach to price indices, leading to an innovative solution in both
a multi-period or a multilateral framework, is presented. The index turns out
to be the generalized least squares solution of a regression model linking
values and quantities of the commodities. The index reference basket, which is
the union of the intersections of the baskets of all country/period taken in
pair, has a coverage broader than extant indices. The properties of the index
are investigated and updating formulas established. Applications to both real
and simulated data provide evidence of the better index performance in
comparison with extant alternatives. | A Novel Multi-Period and Multilateral Price Index | 2021-02-21 09:44:18 | Consuelo Rubina Nava, Maria Grazia Zoia | http://arxiv.org/abs/2102.10528v1, http://arxiv.org/pdf/2102.10528v1 | econ.EM |
29,094 | em | Here, we have analysed a GARCH(1,1) model with the aim to fit higher order
moments for different companies' stock prices. When we assume a gaussian
conditional distribution, we fail to capture any empirical data when fitting
the first three even moments of financial time series. We show instead that a
double gaussian conditional probability distribution better captures the higher
order moments of the data. To demonstrate this point, we construct regions
(phase diagrams), in the fourth and sixth order standardised moment space,
where a GARCH(1,1) model can be used to fit these moments and compare them with
the corresponding moments from empirical data for different sectors of the
economy. We found that the ability of the GARCH model with a double gaussian
conditional distribution to fit higher order moments is dictated by the time
window our data spans. We can only fit data collected within specific time
window lengths and only with certain parameters of the conditional double
gaussian distribution. In order to incorporate the non-stationarity of
financial series, we assume that the parameters of the GARCH model have time
dependence. | Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows | 2021-02-23 14:05:23 | Luke De Clerk, Sergey Savel'ev | http://arxiv.org/abs/2102.11627v4, http://arxiv.org/pdf/2102.11627v4 | econ.EM |
29,095 | em | We propose a computationally feasible way of deriving the identified features
of models with multiple equilibria in pure or mixed strategies. It is shown
that in the case of Shapley regular normal form games, the identified set is
characterized by the inclusion of the true data distribution within the core of
a Choquet capacity, which is interpreted as the generalized likelihood of the
model. In turn, this inclusion is characterized by a finite set of inequalities
and efficient and easily implementable combinatorial methods are described to
check them. In all normal form games, the identified set is characterized in
terms of the value of a submodular or convex optimization program. Efficient
algorithms are then given and compared to check inclusion of a parameter in
this identified set. The latter are illustrated with family bargaining games
and oligopoly entry games. | Set Identification in Models with Multiple Equilibria | 2021-02-24 15:20:11 | Alfred Galichon, Marc Henry | http://dx.doi.org/10.1093/restud/rdr008, http://arxiv.org/abs/2102.12249v1, http://arxiv.org/pdf/2102.12249v1 | econ.EM |
29,096 | em | We provide a test for the specification of a structural model without
identifying assumptions. We show the equivalence of several natural
formulations of correct specification, which we take as our null hypothesis.
From a natural empirical version of the latter, we derive a Kolmogorov-Smirnov
statistic for Choquet capacity functionals, which we use to construct our test.
We derive the limiting distribution of our test statistic under the null, and
show that our test is consistent against certain classes of alternatives. When
the model is given in parametric form, the test can be inverted to yield
confidence regions for the identified parameter set. The approach can be
applied to the estimation of models with sample selection, censored observables
and to games with multiple equilibria. | Inference in Incomplete Models | 2021-02-24 15:39:52 | Alfred Galichon, Marc Henry | http://arxiv.org/abs/2102.12257v1, http://arxiv.org/pdf/2102.12257v1 | econ.EM |
29,097 | em | This paper estimates the break point for large-dimensional factor models with
a single structural break in factor loadings at a common unknown date. First,
we propose a quasi-maximum likelihood (QML) estimator of the change point based
on the second moments of factors, which are estimated by principal component
analysis. We show that the QML estimator performs consistently when the
covariance matrix of the pre- or post-break factor loading, or both, is
singular. When the loading matrix undergoes a rotational type of change while
the number of factors remains constant over time, the QML estimator incurs a
stochastically bounded estimation error. In this case, we establish an
asymptotic distribution of the QML estimator. The simulation results validate
the feasibility of this estimator when used in finite samples. In addition, we
demonstrate empirical applications of the proposed method by applying it to
estimate the break points in a U.S. macroeconomic dataset and a stock return
dataset. | Quasi-maximum likelihood estimation of break point in high-dimensional factor models | 2021-02-25 06:43:18 | Jiangtao Duan, Jushan Bai, Xu Han | http://arxiv.org/abs/2102.12666v3, http://arxiv.org/pdf/2102.12666v3 | econ.EM |
29,098 | em | We propose a new control function (CF) method to estimate a binary response
model in a triangular system with multiple unobserved heterogeneities The CFs
are the expected values of the heterogeneity terms in the reduced form
equations conditional on the histories of the endogenous and the exogenous
variables. The method requires weaker restrictions compared to CF methods with
similar imposed structures. If the support of endogenous regressors is large,
average partial effects are point-identified even when instruments are
discrete. Bounds are provided when the support assumption is violated. An
application and Monte Carlo experiments compare several alternative methods
with ours. | A Control Function Approach to Estimate Panel Data Binary Response Model | 2021-02-25 18:26:41 | Amaresh K Tiwari | http://dx.doi.org/10.1080/07474938.2021.1983328, http://arxiv.org/abs/2102.12927v2, http://arxiv.org/pdf/2102.12927v2 | econ.EM |
29,099 | em | This paper proposes an empirical method to implement the recentered influence
function (RIF) regression of Firpo, Fortin and Lemieux (2009), a relevant
method to study the effect of covariates on many statistics beyond the mean. In
empirically relevant situations where the influence function is not available
or difficult to compute, we suggest to use the \emph{sensitivity curve} (Tukey,
1977) as a feasible alternative. This may be computationally cumbersome when
the sample size is large. The relevance of the proposed strategy derives from
the fact that, under general conditions, the sensitivity curve converges in
probability to the influence function. In order to save computational time we
propose to use a cubic splines non-parametric method for a random subsample and
then to interpolate to the rest of the cases where it was not computed. Monte
Carlo simulations show good finite sample properties. We illustrate the
proposed estimator with an application to the polarization index of Duclos,
Esteban and Ray (2004). | RIF Regression via Sensitivity Curves | 2021-12-02 20:24:43 | Javier Alejo, Gabriel Montes-Rojas, Walter Sosa-Escudero | http://arxiv.org/abs/2112.01435v1, http://arxiv.org/pdf/2112.01435v1 | econ.EM |
29,119 | em | Startups have become in less than 50 years a major component of innovation
and economic growth. An important feature of the startup phenomenon has been
the wealth created through equity in startups to all stakeholders. These
include the startup founders, the investors, and also the employees through the
stock-option mechanism and universities through licenses of intellectual
property. In the employee group, the allocation to important managers like the
chief executive, vice-presidents and other officers, and independent board
members is also analyzed. This report analyzes how equity was allocated in more
than 400 startups, most of which had filed for an initial public offering. The
author has the ambition of informing a general audience about best practice in
equity split, in particular in Silicon Valley, the central place for startup
innovation. | Equity in Startups | 2017-11-02 12:33:44 | Hervé Lebret | http://arxiv.org/abs/1711.00661v1, http://arxiv.org/pdf/1711.00661v1 | econ.EM |
29,100 | em | We study estimation of factor models in a fixed-T panel data setting and
significantly relax the common correlated effects (CCE) assumptions pioneered
by Pesaran (2006) and used in dozens of papers since. In the simplest case, we
model the unobserved factors as functions of the cross-sectional averages of
the explanatory variables and show that this is implied by Pesaran's
assumptions when the number of factors does not exceed the number of
explanatory variables. Our approach allows discrete explanatory variables and
flexible functional forms in the covariates. Plus, it extends to a framework
that easily incorporates general functions of cross-sectional moments, in
addition to heterogeneous intercepts and time trends. Our proposed estimators
include Pesaran's pooled correlated common effects (CCEP) estimator as a
special case. We also show that in the presence of heterogeneous slopes our
estimator is consistent under assumptions much weaker than those previously
used. We derive the fixed-T asymptotic normality of a general estimator and
show how to adjust for estimation of the population moments in the factor
loading equation. | Simple Alternatives to the Common Correlated Effects Model | 2021-12-02 21:37:52 | Nicholas L. Brown, Peter Schmidt, Jeffrey M. Wooldridge | http://dx.doi.org/10.13140/RG.2.2.12655.76969/1, http://arxiv.org/abs/2112.01486v1, http://arxiv.org/pdf/2112.01486v1 | econ.EM |
29,101 | em | Until recently, there has been a consensus that clinicians should condition
patient risk assessments on all observed patient covariates with predictive
power. The broad idea is that knowing more about patients enables more accurate
predictions of their health risks and, hence, better clinical decisions. This
consensus has recently unraveled with respect to a specific covariate, namely
race. There have been increasing calls for race-free risk assessment, arguing
that using race to predict patient outcomes contributes to racial disparities
and inequities in health care. Writers calling for race-free risk assessment
have not studied how it would affect the quality of clinical decisions.
Considering the matter from the patient-centered perspective of medical
economics yields a disturbing conclusion: Race-free risk assessment would harm
patients of all races. | Patient-Centered Appraisal of Race-Free Clinical Risk Assessment | 2021-12-03 02:37:07 | Charles F. Manski | http://arxiv.org/abs/2112.01639v2, http://arxiv.org/pdf/2112.01639v2 | econ.EM |
29,102 | em | We develop a non-parametric multivariate time series model that remains
agnostic on the precise relationship between a (possibly) large set of
macroeconomic time series and their lagged values. The main building block of
our model is a Gaussian process prior on the functional relationship that
determines the conditional mean of the model, hence the name of Gaussian
process vector autoregression (GP-VAR). A flexible stochastic volatility
specification is used to provide additional flexibility and control for
heteroskedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out
through an efficient and scalable algorithm which can handle large models. The
GP-VAR is illustrated by means of simulated data and in a forecasting exercise
with US data. Moreover, we use the GP-VAR to analyze the effects of
macroeconomic uncertainty, with a particular emphasis on time variation and
asymmetries in the transmission mechanisms. | Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty | 2021-12-03 19:16:10 | Niko Hauzenberger, Florian Huber, Massimiliano Marcellino, Nico Petz | http://arxiv.org/abs/2112.01995v3, http://arxiv.org/pdf/2112.01995v3 | econ.EM |
29,103 | em | Despite the widespread use of graphs in empirical research, little is known
about readers' ability to process the statistical information they are meant to
convey ("visual inference"). We study visual inference within the context of
regression discontinuity (RD) designs by measuring how accurately readers
identify discontinuities in graphs produced from data generating processes
calibrated on 11 published papers from leading economics journals. First, we
assess the effects of different graphical representation methods on visual
inference using randomized experiments. We find that bin widths and fit lines
have the largest impacts on whether participants correctly perceive the
presence or absence of a discontinuity. Our experimental results allow us to
make evidence-based recommendations to practitioners, and we suggest using
small bins with no fit lines as a starting point to construct RD graphs.
Second, we compare visual inference on graphs constructed using our preferred
method with widely used econometric inference procedures. We find that visual
inference achieves similar or lower type I error (false positive) rates and
complements econometric inference. | Visual Inference and Graphical Representation in Regression Discontinuity Designs | 2021-12-06 18:02:14 | Christina Korting, Carl Lieberman, Jordan Matsudaira, Zhuan Pei, Yi Shen | http://arxiv.org/abs/2112.03096v2, http://arxiv.org/pdf/2112.03096v2 | econ.EM |
29,104 | em | The `paradox of progress' is an empirical regularity that associates more
education with larger income inequality. Two driving and competing factors
behind this phenomenon are the convexity of the `Mincer equation' (that links
wages and education) and the heterogeneity in its returns, as captured by
quantile regressions. We propose a joint least-squares and quantile regression
statistical framework to derive a decomposition in order to evaluate the
relative contribution of each explanation. The estimators are based on the
`functional derivative' approach. We apply the proposed decomposition strategy
to the case of Argentina 1992 to 2015. | A decomposition method to evaluate the `paradox of progress' with evidence for Argentina | 2021-12-07 20:20:26 | Javier Alejo, Leonardo Gasparini, Gabriel Montes-Rojas, Walter Sosa-Escudero | http://arxiv.org/abs/2112.03836v1, http://arxiv.org/pdf/2112.03836v1 | econ.EM |
29,105 | em | Linear regressions with period and group fixed effects are widely used to
estimate policies' effects: 26 of the 100 most cited papers published by the
American Economic Review from 2015 to 2019 estimate such regressions. It has
recently been shown that those regressions may produce misleading estimates, if
the policy's effect is heterogeneous between groups or over time, as is often
the case. This survey reviews a fast-growing literature that documents this
issue, and that proposes alternative estimators robust to heterogeneous
effects. We use those alternative estimators to revisit Wolfers (2006). | Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey | 2021-12-08 23:14:26 | Clément de Chaisemartin, Xavier D'Haultfœuille | http://arxiv.org/abs/2112.04565v6, http://arxiv.org/pdf/2112.04565v6 | econ.EM |
29,126 | em | Some empirical results are more likely to be published than others. Such
selective publication leads to biased estimates and distorted inference. This
paper proposes two approaches for identifying the conditional probability of
publication as a function of a study's results, the first based on systematic
replication studies and the second based on meta-studies. For known conditional
publication probabilities, we propose median-unbiased estimators and associated
confidence sets that correct for selective publication. We apply our methods to
recent large-scale replication studies in experimental economics and
psychology, and to meta-studies of the effects of minimum wages and de-worming
programs. | Identification of and correction for publication bias | 2017-11-28 22:45:36 | Isaiah Andrews, Maximilian Kasy | http://arxiv.org/abs/1711.10527v1, http://arxiv.org/pdf/1711.10527v1 | econ.EM |
29,106 | em | I suggest an enhancement of the procedure of Chiong, Hsieh, and Shum (2017)
for calculating bounds on counterfactual demand in semiparametric discrete
choice models. Their algorithm relies on a system of inequalities indexed by
cycles of a large number $M$ of observed markets and hence seems to require
computationally infeasible enumeration of all such cycles. I show that such
enumeration is unnecessary because solving the "fully efficient" inequality
system exploiting cycles of all possible lengths $K=1,\dots,M$ can be reduced
to finding the length of the shortest path between every pair of vertices in a
complete bidirected weighted graph on $M$ vertices. The latter problem can be
solved using the Floyd--Warshall algorithm with computational complexity
$O\left(M^3\right)$, which takes only seconds to run even for thousands of
markets. Monte Carlo simulations illustrate the efficiency gain from using
cycles of all lengths, which turns out to be positive, but small. | Efficient counterfactual estimation in semiparametric discrete choice models: a note on Chiong, Hsieh, and Shum (2017) | 2021-12-09 03:49:56 | Grigory Franguridi | http://arxiv.org/abs/2112.04637v1, http://arxiv.org/pdf/2112.04637v1 | econ.EM |
29,107 | em | This study contributes a house price prediction model selection in Tehran
City based on the area between Lorenz curve (LC) and concentration curve (CC)
of the predicted price by using 206,556 observed transaction data over the
period from March 21, 2018, to February 19, 2021. Several different methods
such as generalized linear models (GLM) and recursive partitioning and
regression trees (RPART), random forests (RF) regression models, and neural
network (NN) models were examined house price prediction. We used 90% of all
data samples which were chosen randomly to estimate the parameters of pricing
models and 10% of remaining datasets to test the accuracy of prediction.
Results showed that the area between the LC and CC curves (which are known as
ABC criterion) of real and predicted prices in the test data sample of the
random forest regression model was less than by other models under study. The
comparison of the calculated ABC criteria leads us to conclude that the
nonlinear regression models such as RF regression models give an accurate
prediction of house prices in Tehran City. | Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market | 2021-12-12 12:44:28 | Mohammad Mirbagherijam | http://arxiv.org/abs/2112.06192v1, http://arxiv.org/pdf/2112.06192v1 | econ.EM |
29,108 | em | A new Stata command, ldvqreg, is developed to estimate quantile regression
models for the cases of censored (with lower and/or upper censoring) and binary
dependent variables. The estimators are implemented using a smoothed version of
the quantile regression objective function. Simulation exercises show that it
correctly estimates the parameters and it should be implemented instead of the
available quantile regression methods when censoring is present. An empirical
application to women's labor supply in Uruguay is considered. | Quantile Regression under Limited Dependent Variable | 2021-12-13 20:33:54 | Javier Alejo, Gabriel Montes-Rojas | http://arxiv.org/abs/2112.06822v1, http://arxiv.org/pdf/2112.06822v1 | econ.EM |
29,109 | em | This article presents identification results for the marginal treatment
effect (MTE) when there is sample selection. We show that the MTE is partially
identified for individuals who are always observed regardless of treatment, and
derive uniformly sharp bounds on this parameter under three increasingly
restrictive sets of assumptions. The first result imposes standard MTE
assumptions with an unrestricted sample selection mechanism. The second set of
conditions imposes monotonicity of the sample selection variable with respect
to treatment, considerably shrinking the identified set. Finally, we
incorporate a stochastic dominance assumption which tightens the lower bound
for the MTE. Our analysis extends to discrete instruments. The results rely on
a mixture reformulation of the problem where the mixture weights are
identified, extending Lee's (2009) trimming procedure to the MTE context. We
propose estimators for the bounds derived and use data made available by Deb,
Munking and Trivedi (2006) to empirically illustrate the usefulness of our
approach. | Identifying Marginal Treatment Effects in the Presence of Sample Selection | 2021-12-14 00:08:49 | Otávio Bartalotti, Désiré Kédagni, Vitor Possebom | http://arxiv.org/abs/2112.07014v1, http://arxiv.org/pdf/2112.07014v1 | econ.EM |
29,110 | em | We develop a novel test of the instrumental variable identifying assumptions
for heterogeneous treatment effect models with conditioning covariates. We
assume semiparametric dependence between potential outcomes and conditioning
covariates. This allows us to obtain testable equality and inequality
restrictions among the subdensities of estimable partial residuals. We propose
jointly testing these restrictions. To improve power, we introduce
distillation, where a trimmed sample is used to test the inequality
restrictions. In Monte Carlo exercises we find gains in finite sample power
from testing restrictions jointly and distillation. We apply our test procedure
to three instruments and reject the null for one. | Testing Instrument Validity with Covariates | 2021-12-15 16:06:22 | Thomas Carr, Toru Kitagawa | http://arxiv.org/abs/2112.08092v2, http://arxiv.org/pdf/2112.08092v2 | econ.EM |
29,111 | em | This paper examines the local linear regression (LLR) estimate of the
conditional distribution function $F(y|x)$. We derive three uniform convergence
results: the uniform bias expansion, the uniform convergence rate, and the
uniform asymptotic linear representation. The uniformity in the above results
is with respect to both $x$ and $y$ and therefore has not previously been
addressed in the literature on local polynomial regression. Such uniform
convergence results are especially useful when the conditional distribution
estimator is the first stage of a semiparametric estimator. We demonstrate the
usefulness of these uniform results with two examples: the stochastic
equicontinuity condition in $y$, and the estimation of the integrated
conditional distribution function. | Uniform Convergence Results for the Local Linear Regression Estimation of the Conditional Distribution | 2021-12-16 04:04:23 | Haitian Xie | http://arxiv.org/abs/2112.08546v2, http://arxiv.org/pdf/2112.08546v2 | econ.EM |
29,112 | em | We consider a two-stage estimation method for linear regression that uses the
lasso in Tibshirani (1996) to screen variables and re-estimate the coefficients
using the least-squares boosting method in Friedman (2001) on every set of
selected variables. Based on the large-scale simulation experiment in Hastie et
al. (2020), the performance of lassoed boosting is found to be as competitive
as the relaxed lasso in Meinshausen (2007) and can yield a sparser model under
certain scenarios. An application to predict equity returns also shows that
lassoed boosting can give the smallest mean square prediction error among all
methods under consideration. | Lassoed Boosting and Linear Prediction in Equities Market | 2021-12-16 18:00:37 | Xiao Huang | http://arxiv.org/abs/2112.08934v2, http://arxiv.org/pdf/2112.08934v2 | econ.EM |
29,113 | em | This paper studies the robustness of estimated policy effects to changes in
the distribution of covariates. Robustness to covariate shifts is important,
for example, when evaluating the external validity of quasi-experimental
results, which are often used as a benchmark for evidence-based policy-making.
I propose a novel scalar robustness metric. This metric measures the magnitude
of the smallest covariate shift needed to invalidate a claim on the policy
effect (for example, $ATE \geq 0$) supported by the quasi-experimental
evidence. My metric links the heterogeneity of policy effects and robustness in
a flexible, nonparametric way and does not require functional form assumptions.
I cast the estimation of the robustness metric as a de-biased GMM problem. This
approach guarantees a parametric convergence rate for the robustness metric
while allowing for machine learning-based estimators of policy effect
heterogeneity (for example, lasso, random forest, boosting, neural nets). I
apply my procedure to the Oregon Health Insurance experiment. I study the
robustness of policy effects estimates of health-care utilization and financial
strain outcomes, relative to a shift in the distribution of context-specific
covariates. Such covariates are likely to differ across US states, making
quantification of robustness an important exercise for adoption of the
insurance policy in states other than Oregon. I find that the effect on
outpatient visits is the most robust among the metrics of health-care
utilization considered. | Robustness, Heterogeneous Treatment Effects and Covariate Shifts | 2021-12-17 02:53:42 | Pietro Emilio Spini | http://arxiv.org/abs/2112.09259v1, http://arxiv.org/pdf/2112.09259v1 | econ.EM |
29,114 | em | Aims: To re-introduce the Heckman model as a valid empirical technique in
alcohol studies. Design: To estimate the determinants of problem drinking using
a Heckman and a two-part estimation model. Psychological and neuro-scientific
studies justify my underlying estimation assumptions and covariate exclusion
restrictions. Higher order tests checking for multicollinearity validate the
use of Heckman over the use of two-part estimation models. I discuss the
generalizability of the two models in applied research. Settings and
Participants: Two pooled national population surveys from 2016 and 2017 were
used: the Behavioral Risk Factor Surveillance Survey (BRFS), and the National
Survey of Drug Use and Health (NSDUH). Measurements: Participation in problem
drinking and meeting the criteria for problem drinking. Findings: Both U.S.
national surveys perform well with the Heckman model and pass all higher order
tests. The Heckman model corrects for selection bias and reveals the direction
of bias, where the two-part model does not. For example, the coefficients on
age are upward biased and unemployment is downward biased in the two-part where
the Heckman model does not have a selection bias. Covariate exclusion
restrictions are sensitive to survey conditions and are contextually
generalizable. Conclusions: The Heckman model can be used for alcohol (smoking
studies as well) if the underlying estimation specification passes higher order
tests for multicollinearity and the exclusion restrictions are justified with
integrity for the data used. Its use is merit-worthy because it corrects for
and reveals the direction and the magnitude of selection bias where the
two-part does not. | Heckman-Selection or Two-Part models for alcohol studies? Depends | 2021-12-20 17:08:35 | Reka Sundaram-Stukel | http://arxiv.org/abs/2112.10542v2, http://arxiv.org/pdf/2112.10542v2 | econ.EM |
29,115 | em | We study the Stigler model of citation flows among journals adapting the
pairwise comparison model of Bradley and Terry to do ranking and selection of
journal influence based on nonparametric empirical Bayes procedures.
Comparisons with several other rankings are made. | Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis | 2021-12-21 12:46:29 | Jiaying Gu, Roger Koenker | http://arxiv.org/abs/2112.11064v1, http://arxiv.org/pdf/2112.11064v1 | econ.EM |
29,116 | em | We ask if there are alternative contest models that minimize error or
information loss from misspecification and outperform the Pythagorean model.
This article aims to use simulated data to select the optimal expected win
percentage model among the choice of relevant alternatives. The choices include
the traditional Pythagorean model and the difference-form contest success
function (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season for
the purpose of estimating and analyzing alternative models of expected win
percentage (team quality). We use the open-source, Strategic Baseball Simulator
and develop an AutoHotKey script that programmatically executes the SBS
application, chooses the correct settings for the 2014 season, enters a unique
ID for the simulation data file, and iterates these steps 1,000 times. We
estimate expected win percentage using the traditional Pythagorean model, as
well as the difference-form CSF model that is used in game theory and public
choice economics. Each model is estimated while accounting for fixed (team)
effects. We find that the difference-form CSF model outperforms the traditional
Pythagorean model in terms of explanatory power and in terms of
misspecification-based information loss as estimated by the Akaike Information
Criterion. Through parametric estimation, we further confirm that the simulator
yields realistic statistical outcomes. The simulation methodology offers the
advantage of greatly improved sample size. As the season is held constant, our
simulation-based statistical inference also allows for estimation and model
comparison without the (time series) issue of non-stationarity. The results
suggest that improved win (productivity) estimation can be achieved through
alternative CSF specifications. | An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations | 2021-12-30 01:08:24 | Justin Ehrlich, Christopher Boudreaux, James Boudreau, Shane Sanders | http://arxiv.org/abs/2112.14846v1, http://arxiv.org/pdf/2112.14846v1 | econ.EM |
29,117 | em | In this paper we examine the relation between market returns and volatility
measures through machine learning methods in a high-frequency environment. We
implement a minute-by-minute rolling window intraday estimation method using
two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random
Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the
strongest candidate predictor for intraday market returns in our analysis,
specially when implemented through the LSTM model. This model also improves
significantly the performance of the lagged market return as predictive
variable. Finally, intraday RF estimation outputs indicate that there is no
performance improvement with this method, and it may even worsen the results in
some cases. | Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach | 2021-12-30 19:05:17 | Iuri H. Ferreira, Marcelo C. Medeiros | http://arxiv.org/abs/2112.15108v1, http://arxiv.org/pdf/2112.15108v1 | econ.EM |
29,118 | em | Startups have become in less than 50 years a major component of innovation
and economic growth. Silicon Valley has been the place where the startup
phenomenon was the most obvious and Stanford University was a major component
of that success. Companies such as Google, Yahoo, Sun Microsystems, Cisco,
Hewlett Packard had very strong links with Stanford but even these vary famous
success stories cannot fully describe the richness and diversity of the
Stanford entrepreneurial activity. This report explores the dynamics of more
than 5000 companies founded by Stanford University alumni and staff, through
their value creation, their field of activities, their growth patterns and
more. The report also explores some features of the founders of these companies
such as their academic background or the number of years between their Stanford
experience and their company creation. | Startups and Stanford University | 2017-11-02 11:14:26 | Hervé Lebret | http://arxiv.org/abs/1711.00644v1, http://arxiv.org/pdf/1711.00644v1 | econ.EM |
29,120 | em | I propose a treatment selection model that introduces unobserved
heterogeneity in both choice sets and preferences to evaluate the average
effects of a program offer. I show how to exploit the model structure to define
parameters capturing these effects and then computationally characterize their
identified sets under instrumental variable variation in choice sets. I
illustrate these tools by analyzing the effects of providing an offer to the
Head Start preschool program using data from the Head Start Impact Study. I
find that such a policy affects a large number of children who take up the
offer, and that they subsequently have positive effects on test scores. These
effects arise from children who do not have any preschool as an outside option.
A cost-benefit analysis reveals that the earning benefits associated with the
test score gains can be large and outweigh the net costs associated with offer
take up. | Identifying the Effects of a Program Offer with an Application to Head Start | 2017-11-06 20:55:59 | Vishal Kamat | http://arxiv.org/abs/1711.02048v6, http://arxiv.org/pdf/1711.02048v6 | econ.EM |
29,121 | em | I study identification, estimation and inference for spillover effects in
experiments where units' outcomes may depend on the treatment assignments of
other units within a group. I show that the commonly-used reduced-form
linear-in-means regression identifies a weighted sum of spillover effects with
some negative weights, and that the difference in means between treated and
controls identifies a combination of direct and spillover effects entering with
different signs. I propose nonparametric estimators for average direct and
spillover effects that overcome these issues and are consistent and
asymptotically normal under a precise relationship between the number of
parameters of interest, the total sample size and the treatment assignment
mechanism. These findings are illustrated using data from a conditional cash
transfer program and with simulations. The empirical results reveal the
potential pitfalls of failing to flexibly account for spillover effects in
policy evaluation: the estimated difference in means and the reduced-form
linear-in-means coefficients are all close to zero and statistically
insignificant, whereas the nonparametric estimators I propose reveal large,
nonlinear and significant spillover effects. | Identification and Estimation of Spillover Effects in Randomized Experiments | 2017-11-08 01:04:44 | Gonzalo Vazquez-Bare | http://arxiv.org/abs/1711.02745v8, http://arxiv.org/pdf/1711.02745v8 | econ.EM |
29,122 | em | Futures market contracts with varying maturities are traded concurrently and
the speed at which they process information is of value in understanding the
pricing discovery process. Using price discovery measures, including Putnins
(2013) information leadership share and intraday data, we quantify the
proportional contribution of price discovery between nearby and deferred
contracts in the corn and live cattle futures markets. Price discovery is more
systematic in the corn than in the live cattle market. On average, nearby
contracts lead all deferred contracts in price discovery in the corn market,
but have a relatively less dominant role in the live cattle market. In both
markets, the nearby contract loses dominance when its relative volume share
dips below 50%, which occurs about 2-3 weeks before expiration in corn and 5-6
weeks before expiration in live cattle. Regression results indicate that the
share of price discovery is most closely linked to trading volume but is also
affected, to far less degree, by time to expiration, backwardation, USDA
announcements and market crashes. The effects of these other factors vary
between the markets which likely reflect the difference in storability as well
as other market-related characteristics. | Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets | 2017-11-09 21:12:05 | Zhepeng Hu, Mindy Mallory, Teresa Serra, Philip Garcia | http://arxiv.org/abs/1711.03506v1, http://arxiv.org/pdf/1711.03506v1 | econ.EM |
29,123 | em | Economic complexity reflects the amount of knowledge that is embedded in the
productive structure of an economy. It resides on the premise of hidden
capabilities - fundamental endowments underlying the productive structure. In
general, measuring the capabilities behind economic complexity directly is
difficult, and indirect measures have been suggested which exploit the fact
that the presence of the capabilities is expressed in a country's mix of
products. We complement these studies by introducing a probabilistic framework
which leverages Bayesian non-parametric techniques to extract the dominant
features behind the comparative advantage in exported products. Based on
economic evidence and trade data, we place a restricted Indian Buffet Process
on the distribution of countries' capability endowment, appealing to a culinary
metaphor to model the process of capability acquisition. The approach comes
with a unique level of interpretability, as it produces a concise and
economically plausible description of the instantiated capabilities. | Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies | 2017-11-17 17:09:19 | Zoran Utkovski, Melanie F. Pradier, Viktor Stojkoski, Fernando Perez-Cruz, Ljupco Kocarev | http://dx.doi.org/10.1371/journal.pone.0200822, http://arxiv.org/abs/1711.07327v2, http://arxiv.org/pdf/1711.07327v2 | econ.EM |
29,124 | em | This study briefly introduces the development of Shantou Special Economic
Zone under Reform and Opening-Up Policy from 1980 through 2016 with a focus on
policy making issues and its influences on local economy. This paper is divided
into two parts, 1980 to 1991, 1992 to 2016 in accordance with the separation of
the original Shantou District into three cities: Shantou, Chaozhou and Jieyang
in the end of 1991. This study analyzes the policy making issues in the
separation of the original Shantou District, the influences of the policy on
Shantou's economy after separation, the possibility of merging the three cities
into one big new economic district in the future and reasons that lead to the
stagnant development of Shantou in recent 20 years. This paper uses statistical
longitudinal analysis in analyzing economic problems with applications of
non-parametric statistics through generalized additive model and time series
forecasting methods. The paper is authored by Bowen Cai solely, who is the
graduate student in the PhD program of Applied and Computational Mathematics
and Statistics at the University of Notre Dame with concentration in big data
analysis. | The Research on the Stagnant Development of Shantou Special Economic Zone Under Reform and Opening-Up Policy | 2017-11-24 09:34:15 | Bowen Cai | http://arxiv.org/abs/1711.08877v1, http://arxiv.org/pdf/1711.08877v1 | econ.EM |
29,125 | em | This paper presents the identification of heterogeneous elasticities in the
Cobb-Douglas production function. The identification is constructive with
closed-form formulas for the elasticity with respect to each input for each
firm. We propose that the flexible input cost ratio plays the role of a control
function under "non-collinear heterogeneity" between elasticities with respect
to two flexible inputs. The ex ante flexible input cost share can be used to
identify the elasticities with respect to flexible inputs for each firm. The
elasticities with respect to labor and capital can be subsequently identified
for each firm under the timing assumption admitting the functional
independence. | Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function | 2017-11-28 01:51:57 | Tong Li, Yuya Sasaki | http://arxiv.org/abs/1711.10031v1, http://arxiv.org/pdf/1711.10031v1 | econ.EM |
29,127 | em | Research on growing American political polarization and antipathy primarily
studies public institutions and political processes, ignoring private effects
including strained family ties. Using anonymized smartphone-location data and
precinct-level voting, we show that Thanksgiving dinners attended by
opposing-party precinct residents were 30-50 minutes shorter than same-party
dinners. This decline from a mean of 257 minutes survives extensive spatial and
demographic controls. Dinner reductions in 2016 tripled for travelers from
media markets with heavy political advertising --- an effect not observed in
2015 --- implying a relationship to election-related behavior. Effects appear
asymmetric: while fewer Democratic-precinct residents traveled in 2016 than
2015, political differences shortened Thanksgiving dinners more among
Republican-precinct residents. Nationwide, 34 million person-hours of
cross-partisan Thanksgiving discourse were lost in 2016 to partisan effects. | The Effect of Partisanship and Political Advertising on Close Family Ties | 2017-11-29 01:58:02 | M. Keith Chen, Ryne Rohla | http://dx.doi.org/10.1126/science.aaq1433, http://arxiv.org/abs/1711.10602v2, http://arxiv.org/pdf/1711.10602v2 | econ.EM |
29,128 | em | The main purpose of this paper is to analyze threshold effects of official
development assistance (ODA) on economic growth in WAEMU zone countries. To
achieve this, the study is based on OECD and WDI data covering the period
1980-2015 and used Hansen's Panel Threshold Regression (PTR) model to
"bootstrap" aid threshold above which its effectiveness is effective. The
evidence strongly supports the view that the relationship between aid and
economic growth is non-linear with a unique threshold which is 12.74% GDP.
Above this value, the marginal effect of aid is 0.69 points, "all things being
equal to otherwise". One of the main contribution of this paper is to show that
WAEMU countries need investments that could be covered by the foreign aid. This
later one should be considered just as a complementary resource. Thus, WEAMU
countries should continue to strengthen their efforts in internal resource
mobilization in order to fulfil this need. | Aide et Croissance dans les pays de l'Union Economique et Mon{é}taire Ouest Africaine (UEMOA) : retour sur une relation controvers{é}e | 2018-04-13 16:07:11 | Nimonka Bayale | http://arxiv.org/abs/1805.00435v1, http://arxiv.org/pdf/1805.00435v1 | econ.EM |
29,129 | em | In this paper, I endeavour to construct a new model, by extending the classic
exogenous economic growth model by including a measurement which tries to
explain and quantify the size of technological innovation ( A ) endogenously. I
do not agree technology is a "constant" exogenous variable, because it is
humans who create all technological innovations, and it depends on how much
human and physical capital is allocated for its research. I inspect several
possible approaches to do this, and then I test my model both against sample
and real world evidence data. I call this method "dynamic" because it tries to
model the details in resource allocations between research, labor and capital,
by affecting each other interactively. In the end, I point out which is the new
residual and the parts of the economic growth model which can be further
improved. | Endogenous growth - A dynamic technology augmentation of the Solow model | 2018-05-02 11:23:18 | Murad Kasim | http://arxiv.org/abs/1805.00668v1, http://arxiv.org/pdf/1805.00668v1 | econ.EM |
29,130 | em | This paper studies the identification and estimation of the optimal linear
approximation of a structural regression function. The parameter in the linear
approximation is called the Optimal Linear Instrumental Variables Approximation
(OLIVA). This paper shows that a necessary condition for standard inference on
the OLIVA is also sufficient for the existence of an IV estimand in a linear
model. The instrument in the IV estimand is unknown and may not be identified.
A Two-Step IV (TSIV) estimator based on Tikhonov regularization is proposed,
which can be implemented by standard regression routines. We establish the
asymptotic normality of the TSIV estimator assuming neither completeness nor
identification of the instrument. As an important application of our analysis,
we robustify the classical Hausman test for exogeneity against misspecification
of the linear structural model. We also discuss extensions to weighted least
squares criteria. Monte Carlo simulations suggest an excellent finite sample
performance for the proposed inferences. Finally, in an empirical application
estimating the elasticity of intertemporal substitution (EIS) with US data, we
obtain TSIV estimates that are much larger than their standard IV counterparts,
with our robust Hausman test failing to reject the null hypothesis of
exogeneity of real interest rates. | Optimal Linear Instrumental Variables Approximations | 2018-05-08 23:44:27 | Juan Carlos Escanciano, Wei Li | http://arxiv.org/abs/1805.03275v3, http://arxiv.org/pdf/1805.03275v3 | econ.EM |
29,131 | em | We study the identification and estimation of structural parameters in
dynamic panel data logit models where decisions are forward-looking and the
joint distribution of unobserved heterogeneity and observable state variables
is nonparametric, i.e., fixed-effects model. We consider models with two
endogenous state variables: the lagged decision variable, and the time duration
in the last choice. This class of models includes as particular cases important
economic applications such as models of market entry-exit, occupational choice,
machine replacement, inventory and investment decisions, or dynamic demand of
differentiated products. The identification of structural parameters requires a
sufficient statistic that controls for unobserved heterogeneity not only in
current utility but also in the continuation value of the forward-looking
decision problem. We obtain the minimal sufficient statistic and prove
identification of some structural parameters using a conditional likelihood
approach. We apply this estimator to a machine replacement model. | Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models | 2018-05-10 19:27:33 | Victor Aguirregabiria, Jiaying Gu, Yao Luo | http://arxiv.org/abs/1805.04048v1, http://arxiv.org/pdf/1805.04048v1 | econ.EM |
29,132 | em | This paper constructs individual-specific density forecasts for a panel of
firms or households using a dynamic linear model with common and heterogeneous
coefficients as well as cross-sectional heteroskedasticity. The panel
considered in this paper features a large cross-sectional dimension N but short
time series T. Due to the short T, traditional methods have difficulty in
disentangling the heterogeneous parameters from the shocks, which contaminates
the estimates of the heterogeneous parameters. To tackle this problem, I assume
that there is an underlying distribution of heterogeneous parameters, model
this distribution nonparametrically allowing for correlation between
heterogeneous parameters and initial conditions as well as individual-specific
regressors, and then estimate this distribution by combining information from
the whole panel. Theoretically, I prove that in cross-sectional homoskedastic
cases, both the estimated common parameters and the estimated distribution of
the heterogeneous parameters achieve posterior consistency, and that the
density forecasts asymptotically converge to the oracle forecast.
Methodologically, I develop a simulation-based posterior sampling algorithm
specifically addressing the nonparametric density estimation of unobserved
heterogeneous parameters. Monte Carlo simulations and an empirical application
to young firm dynamics demonstrate improvements in density forecasts relative
to alternative approaches. | Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective | 2018-05-10 23:51:01 | Laura Liu | http://arxiv.org/abs/1805.04178v3, http://arxiv.org/pdf/1805.04178v3 | econ.EM |
29,134 | em | This paper contributes to the literature on treatment effects estimation with
machine learning inspired methods by studying the performance of different
estimators based on the Lasso. Building on recent work in the field of
high-dimensional statistics, we use the semiparametric efficient score
estimation structure to compare different estimators. Alternative weighting
schemes are considered and their suitability for the incorporation of machine
learning estimators is assessed using theoretical arguments and various Monte
Carlo experiments. Additionally we propose an own estimator based on doubly
robust Kernel matching that is argued to be more robust to nuisance parameter
misspecification. In the simulation study we verify theory based intuition and
find good finite sample properties of alternative weighting scheme estimators
like the one we propose. | The Finite Sample Performance of Treatment Effects Estimators based on the Lasso | 2018-05-14 11:50:54 | Michael Zimmert | http://arxiv.org/abs/1805.05067v1, http://arxiv.org/pdf/1805.05067v1 | econ.EM |
29,135 | em | This paper introduces a method for linking technological improvement rates
(i.e. Moore's Law) and technology adoption curves (i.e. S-Curves). There has
been considerable research surrounding Moore's Law and the generalized versions
applied to the time dependence of performance for other technologies. The prior
work has culminated with methodology for quantitative estimation of
technological improvement rates for nearly any technology. This paper examines
the implications of such regular time dependence for performance upon the
timing of key events in the technological adoption process. We propose a simple
crossover point in performance which is based upon the technological
improvement rates and current level differences for target and replacement
technologies. The timing for the cross-over is hypothesized as corresponding to
the first 'knee'? in the technology adoption "S-curve" and signals when the
market for a given technology will start to be rewarding for innovators. This
is also when potential entrants are likely to intensely experiment with
product-market fit and when the competition to achieve a dominant design
begins. This conceptual framework is then back-tested by examining two
technological changes brought about by the internet, namely music and video
transmission. The uncertainty analysis around the cases highlight opportunities
for organizations to reduce future technological uncertainty. Overall, the
results from the case studies support the reliability and utility of the
conceptual framework in strategic business decision-making with the caveat that
while technical uncertainty is reduced, it is not eliminated. | Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies | 2018-05-16 17:09:04 | Christopher L. Benson, Christopher L. Magee | http://arxiv.org/abs/1805.06339v1, http://arxiv.org/pdf/1805.06339v1 | econ.EM |
29,136 | em | Some aspects of the problem of stable marriage are discussed. There are two
distinguished marriage plans: the fully transferable case, where money can be
transferred between the participants, and the fully non transferable case where
each participant has its own rigid preference list regarding the other gender.
We continue to discuss intermediate partial transferable cases. Partial
transferable plans can be approached as either special cases of cooperative
games using the notion of a core, or as a generalization of the cyclical
monotonicity property of the fully transferable case (fake promises). We shall
introduced these two approaches, and prove the existence of stable marriage for
the fully transferable and non-transferable plans. | Happy family of stable marriages | 2018-05-17 13:33:04 | Gershon Wolansky | http://arxiv.org/abs/1805.06687v1, http://arxiv.org/pdf/1805.06687v1 | econ.EM |
29,137 | em | This study back-tests a marginal cost of production model proposed to value
the digital currency bitcoin. Results from both conventional regression and
vector autoregression (VAR) models show that the marginal cost of production
plays an important role in explaining bitcoin prices, challenging recent
allegations that bitcoins are essentially worthless. Even with markets pricing
bitcoin in the thousands of dollars each, the valuation model seems robust. The
data show that a price bubble that began in the Fall of 2017 resolved itself in
early 2018, converging with the marginal cost model. This suggests that while
bubbles may appear in the bitcoin market, prices will tend to this bound and
not collapse to zero. | Bitcoin price and its marginal cost of production: support for a fundamental value | 2018-05-19 18:30:29 | Adam Hayes | http://arxiv.org/abs/1805.07610v1, http://arxiv.org/pdf/1805.07610v1 | econ.EM |
29,138 | em | The issue of model selection in applied research is of vital importance.
Since the true model in such research is not known, which model should be used
from among various potential ones is an empirical question. There might exist
several competitive models. A typical approach to dealing with this is classic
hypothesis testing using an arbitrarily chosen significance level based on the
underlying assumption that a true null hypothesis exists. In this paper we
investigate how successful this approach is in determining the correct model
for different data generating processes using time series data. An alternative
approach based on more formal model selection techniques using an information
criterion or cross-validation is suggested and evaluated in the time series
environment via Monte Carlo experiments. This paper also explores the
effectiveness of deciding what type of general relation exists between two
variables (e.g. relation in levels or relation in first differences) using
various strategies based on hypothesis testing and on information criteria with
the presence or absence of unit roots. | Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing | 2018-05-23 10:40:53 | R. Scott Hacker, Abdulnasser Hatemi-J | http://arxiv.org/abs/1805.08991v1, http://arxiv.org/pdf/1805.08991v1 | econ.EM |
29,139 | em | This study investigates the dose-response effects of making music on youth
development. Identification is based on the conditional independence assumption
and estimation is implemented using a recent double machine learning estimator.
The study proposes solutions to two highly practically relevant questions that
arise for these new methods: (i) How to investigate sensitivity of estimates to
tuning parameter choices in the machine learning part? (ii) How to assess
covariate balancing in high-dimensional settings? The results show that
improvements in objectively measured cognitive skills require at least medium
intensity, while improvements in school grades are already observed for low
intensity of practice. | A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills | 2018-05-23 10:58:08 | Michael C. Knaus | http://arxiv.org/abs/1805.10300v2, http://arxiv.org/pdf/1805.10300v2 | econ.EM |
29,932 | em | We propose logit-based IV and augmented logit-based IV estimators that serve
as alternatives to the traditionally used 2SLS estimator in the model where
both the endogenous treatment variable and the corresponding instrument are
binary. Our novel estimators are as easy to compute as the 2SLS estimator but
have an advantage over the 2SLS estimator in terms of causal interpretability.
In particular, in certain cases where the probability limits of both our
estimators and the 2SLS estimator take the form of weighted-average treatment
effects, our estimators are guaranteed to yield non-negative weights whereas
the 2SLS estimator is not. | Logit-based alternatives to two-stage least squares | 2023-12-16 08:47:43 | Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Shuyang Sheng | http://arxiv.org/abs/2312.10333v1, http://arxiv.org/pdf/2312.10333v1 | econ.EM |
29,140 | em | This article introduces two absolutely continuous global-local shrinkage
priors to enable stochastic variable selection in the context of
high-dimensional matrix exponential spatial specifications. Existing approaches
as a means to dealing with overparameterization problems in spatial
autoregressive specifications typically rely on computationally demanding
Bayesian model-averaging techniques. The proposed shrinkage priors can be
implemented using Markov chain Monte Carlo methods in a flexible and efficient
way. A simulation study is conducted to evaluate the performance of each of the
shrinkage priors. Results suggest that they perform particularly well in
high-dimensional environments, especially when the number of parameters to
estimate exceeds the number of observations. For an empirical illustration we
use pan-European regional economic growth data. | Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models | 2018-05-28 12:01:55 | Michael Pfarrhofer, Philipp Piribauer | http://dx.doi.org/10.1016/j.spasta.2018.10.004, http://arxiv.org/abs/1805.10822v1, http://arxiv.org/pdf/1805.10822v1 | econ.EM |
29,141 | em | We propose a method that reconciles two popular approaches to structural
estimation and inference: Using a complete - yet approximate model versus
imposing a set of credible behavioral conditions. This is done by distorting
the approximate model to satisfy these conditions. We provide the asymptotic
theory and Monte Carlo evidence, and illustrate that counterfactual experiments
are possible. We apply the methodology to the model of long run risks in
aggregate consumption (Bansal and Yaron, 2004), where the complete model is
generated using the Campbell and Shiller (1988) approximation. Using US data,
we investigate the empirical importance of the neglected non-linearity. We find
that distorting the model to satisfy the non-linear equilibrium condition is
strongly preferred by the data while the quality of the approximation is yet
another reason for the downward bias to estimates of the intertemporal
elasticity of substitution and the upward bias in risk aversion. | Equilibrium Restrictions and Approximate Models -- With an application to Pricing Macroeconomic Risk | 2018-05-28 14:27:20 | Andreas Tryphonides | http://arxiv.org/abs/1805.10869v3, http://arxiv.org/pdf/1805.10869v3 | econ.EM |
29,142 | em | The United States' power market is featured by the lack of judicial power at
the federal level. The market thus provides a unique testing environment for
the market organization structure. At the same time, the econometric modeling
and forecasting of electricity market consumption become more challenging.
Import and export, which generally follow simple rules in European countries,
can be a result of direct market behaviors. This paper seeks to build a general
model for power consumption and using the model to test several hypotheses. | Modeling the residential electricity consumption within a restructured power market | 2018-05-28 22:19:00 | Chelsea Sun | http://arxiv.org/abs/1805.11138v2, http://arxiv.org/pdf/1805.11138v2 | econ.EM |
29,143 | em | The policy relevant treatment effect (PRTE) measures the average effect of
switching from a status-quo policy to a counterfactual policy. Estimation of
the PRTE involves estimation of multiple preliminary parameters, including
propensity scores, conditional expectation functions of the outcome and
covariates given the propensity score, and marginal treatment effects. These
preliminary estimators can affect the asymptotic distribution of the PRTE
estimator in complicated and intractable manners. In this light, we propose an
orthogonal score for double debiased estimation of the PRTE, whereby the
asymptotic distribution of the PRTE estimator is obtained without any influence
of preliminary parameter estimators as far as they satisfy mild requirements of
convergence rates. To our knowledge, this paper is the first to develop limit
distribution theories for inference about the PRTE. | Estimation and Inference for Policy Relevant Treatment Effects | 2018-05-29 17:34:35 | Yuya Sasaki, Takuya Ura | http://arxiv.org/abs/1805.11503v4, http://arxiv.org/pdf/1805.11503v4 | econ.EM |
29,144 | em | Partial mean with generated regressors arises in several econometric
problems, such as the distribution of potential outcomes with continuous
treatments and the quantile structural function in a nonseparable triangular
model. This paper proposes a nonparametric estimator for the partial mean
process, where the second step consists of a kernel regression on regressors
that are estimated in the first step. The main contribution is a uniform
expansion that characterizes in detail how the estimation error associated with
the generated regressor affects the limiting distribution of the marginal
integration estimator. The general results are illustrated with two examples:
the generalized propensity score for a continuous treatment (Hirano and Imbens,
2004) and control variables in triangular models (Newey, Powell, and Vella,
1999; Imbens and Newey, 2009). An empirical application to the Job Corps
program evaluation demonstrates the usefulness of the method. | Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models | 2018-11-01 02:37:25 | Ying-Ying Lee | http://arxiv.org/abs/1811.00157v1, http://arxiv.org/pdf/1811.00157v1 | econ.EM |
29,145 | em | I develop a new identification strategy for treatment effects when noisy
measurements of unobserved confounding factors are available. I use proxy
variables to construct a random variable conditional on which treatment
variables become exogenous. The key idea is that, under appropriate conditions,
there exists a one-to-one mapping between the distribution of unobserved
confounding factors and the distribution of proxies. To ensure sufficient
variation in the constructed control variable, I use an additional variable,
termed excluded variable, which satisfies certain exclusion restrictions and
relevance conditions. I establish asymptotic distributional results for
semiparametric and flexible parametric estimators of causal parameters. I
illustrate empirical relevance and usefulness of my results by estimating
causal effects of attending selective college on earnings. | Treatment Effect Estimation with Noisy Conditioning Variables | 2018-11-02 01:53:48 | Kenichi Nagasawa | http://arxiv.org/abs/1811.00667v4, http://arxiv.org/pdf/1811.00667v4 | econ.EM |
29,146 | em | We develop a new statistical procedure to test whether the dependence
structure is identical between two groups. Rather than relying on a single
index such as Pearson's correlation coefficient or Kendall's Tau, we consider
the entire dependence structure by investigating the dependence functions
(copulas). The critical values are obtained by a modified randomization
procedure designed to exploit asymptotic group invariance conditions.
Implementation of the test is intuitive and simple, and does not require any
specification of a tuning parameter or weight function. At the same time, the
test exhibits excellent finite sample performance, with the null rejection
rates almost equal to the nominal level even when the sample size is extremely
small. Two empirical applications concerning the dependence between income and
consumption, and the Brexit effect on European financial market integration are
provided. | Randomization Tests for Equality in Dependence Structure | 2018-11-06 03:59:00 | Juwon Seo | http://arxiv.org/abs/1811.02105v1, http://arxiv.org/pdf/1811.02105v1 | econ.EM |
29,147 | em | Finite mixture models are useful in applied econometrics. They can be used to
model unobserved heterogeneity, which plays major roles in labor economics,
industrial organization and other fields. Mixtures are also convenient in
dealing with contaminated sampling models and models with multiple equilibria.
This paper shows that finite mixture models are nonparametrically identified
under weak assumptions that are plausible in economic applications. The key is
to utilize the identification power implied by information in covariates
variation. First, three identification approaches are presented, under distinct
and non-nested sets of sufficient conditions. Observable features of data
inform us which of the three approaches is valid. These results apply to
general nonparametric switching regressions, as well as to structural
econometric models, such as auction models with unobserved heterogeneity.
Second, some extensions of the identification results are developed. In
particular, a mixture regression where the mixing weights depend on the value
of the regressors in a fully unrestricted manner is shown to be
nonparametrically identifiable. This means a finite mixture model with
function-valued unobserved heterogeneity can be identified in a cross-section
setting, without restricting the dependence pattern between the regressor and
the unobserved heterogeneity. In this aspect it is akin to fixed effects panel
data models which permit unrestricted correlation between unobserved
heterogeneity and covariates. Third, the paper shows that fully nonparametric
estimation of the entire mixture model is possible, by forming a sample
analogue of one of the new identification strategies. The estimator is shown to
possess a desirable polynomial rate of convergence as in a standard
nonparametric estimation problem, despite nonregular features of the model. | Nonparametric Analysis of Finite Mixtures | 2018-11-07 05:16:14 | Yuichi Kitamura, Louise Laage | http://arxiv.org/abs/1811.02727v1, http://arxiv.org/pdf/1811.02727v1 | econ.EM |
29,148 | em | Single index linear models for binary response with random coefficients have
been extensively employed in many econometric settings under various parametric
specifications of the distribution of the random coefficients. Nonparametric
maximum likelihood estimation (NPMLE) as proposed by Cosslett (1983) and
Ichimura and Thompson (1998), in contrast, has received less attention in
applied work due primarily to computational difficulties. We propose a new
approach to computation of NPMLEs for binary response models that significantly
increase their computational tractability thereby facilitating greater
flexibility in applications. Our approach, which relies on recent developments
involving the geometry of hyperplane arrangements, is contrasted with the
recently proposed deconvolution method of Gautier and Kitamura (2013). An
application to modal choice for the journey to work in the Washington DC area
illustrates the methods. | Nonparametric maximum likelihood methods for binary response models with random coefficients | 2018-11-08 12:33:02 | Jiaying Gu, Roger Koenker | http://arxiv.org/abs/1811.03329v3, http://arxiv.org/pdf/1811.03329v3 | econ.EM |
29,149 | em | This study proposes a point estimator of the break location for a one-time
structural break in linear regression models. If the break magnitude is small,
the least-squares estimator of the break date has two modes at the ends of the
finite sample period, regardless of the true break location. To solve this
problem, I suggest an alternative estimator based on a modification of the
least-squares objective function. The modified objective function incorporates
estimation uncertainty that varies across potential break dates. The new break
point estimator is consistent and has a unimodal finite sample distribution
under small break magnitudes. A limit distribution is provided under an in-fill
asymptotic framework. Monte Carlo simulation results suggest that the new
estimator outperforms the least-squares estimator. I apply the method to
estimate the break date in U.S. real GDP growth and U.S. and UK stock return
prediction models. | Estimation of a Structural Break Point in Linear Regression Models | 2018-11-09 03:10:11 | Yaein Baek | http://arxiv.org/abs/1811.03720v3, http://arxiv.org/pdf/1811.03720v3 | econ.EM |
29,150 | em | This paper analyses the use of bootstrap methods to test for parameter change
in linear models estimated via Two Stage Least Squares (2SLS). Two types of
test are considered: one where the null hypothesis is of no change and the
alternative hypothesis involves discrete change at k unknown break-points in
the sample; and a second test where the null hypothesis is that there is
discrete parameter change at l break-points in the sample against an
alternative in which the parameters change at l + 1 break-points. In both
cases, we consider inferences based on a sup-Wald-type statistic using either
the wild recursive bootstrap or the wild fixed bootstrap. We establish the
asymptotic validity of these bootstrap tests under a set of general conditions
that allow the errors to exhibit conditional and/or unconditional
heteroskedasticity, and report results from a simulation study that indicate
the tests yield reliable inferences in the sample sizes often encountered in
macroeconomics. The analysis covers the cases where the first-stage estimation
of 2SLS involves a model whose parameters are either constant or themselves
subject to discrete parameter change. If the errors exhibit unconditional
heteroskedasticity and/or the reduced form is unstable then the bootstrap
methods are particularly attractive because the limiting distributions of the
test statistics are not pivotal. | Bootstrapping Structural Change Tests | 2018-11-09 23:15:33 | Otilia Boldea, Adriana Cornea-Madeira, Alastair R. Hall | http://dx.doi.org/10.1016/j.jeconom.2019.05.019, http://arxiv.org/abs/1811.04125v1, http://arxiv.org/pdf/1811.04125v1 | econ.EM |
29,151 | em | Identification of multinomial choice models is often established by using
special covariates that have full support. This paper shows how these
identification results can be extended to a large class of multinomial choice
models when all covariates are bounded. I also provide a new
$\sqrt{n}$-consistent asymptotically normal estimator of the finite-dimensional
parameters of the model. | Identification and estimation of multinomial choice models with latent special covariates | 2018-11-14 01:48:40 | Nail Kashaev | http://arxiv.org/abs/1811.05555v3, http://arxiv.org/pdf/1811.05555v3 | econ.EM |
29,152 | em | In this paper, we investigate seemingly unrelated regression (SUR) models
that allow the number of equations (N) to be large, and to be comparable to the
number of the observations in each equation (T). It is well known in the
literature that the conventional SUR estimator, for example, the generalized
least squares (GLS) estimator of Zellner (1962) does not perform well. As the
main contribution of the paper, we propose a new feasible GLS estimator called
the feasible graphical lasso (FGLasso) estimator. For a feasible implementation
of the GLS estimator, we use the graphical lasso estimation of the precision
matrix (the inverse of the covariance matrix of the equation system errors)
assuming that the underlying unknown precision matrix is sparse. We derive
asymptotic theories of the new estimator and investigate its finite sample
properties via Monte-Carlo simulations. | Estimation of High-Dimensional Seemingly Unrelated Regression Models | 2018-11-14 02:19:46 | Lidan Tan, Khai X. Chiong, Hyungsik Roger Moon | http://arxiv.org/abs/1811.05567v1, http://arxiv.org/pdf/1811.05567v1 | econ.EM |
29,161 | em | We study partial identification of the preference parameters in the
one-to-one matching model with perfectly transferable utilities. We do so
without imposing parametric distributional assumptions on the unobserved
heterogeneity and with data on one large market. We provide a tractable
characterisation of the identified set under various classes of nonparametric
distributional assumptions on the unobserved heterogeneity. Using our
methodology, we re-examine some of the relevant questions in the empirical
literature on the marriage market, which have been previously studied under the
Logit assumption. Our results reveal that many findings in the aforementioned
literature are primarily driven by such parametric restrictions. | Partial Identification in Matching Models for the Marriage Market | 2019-02-15 00:37:28 | Cristina Gualdani, Shruti Sinha | http://arxiv.org/abs/1902.05610v6, http://arxiv.org/pdf/1902.05610v6 | econ.EM |
29,153 | em | In this study, Bayesian inference is developed for structural vector
autoregressive models in which the structural parameters are identified via
Markov-switching heteroskedasticity. In such a model, restrictions that are
just-identifying in the homoskedastic case, become over-identifying and can be
tested. A set of parametric restrictions is derived under which the structural
matrix is globally or partially identified and a Savage-Dickey density ratio is
used to assess the validity of the identification conditions. The latter is
facilitated by analytical derivations that make the computations fast and
numerical standard errors small. As an empirical example, monetary models are
compared using heteroskedasticity as an additional device for identification.
The empirical results support models with money in the interest rate reaction
function. | Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity | 2018-11-20 13:29:18 | Helmut Lütkepohl, Tomasz Woźniak | http://dx.doi.org/10.1016/j.jedc.2020.103862, http://arxiv.org/abs/1811.08167v1, http://arxiv.org/pdf/1811.08167v1 | econ.EM |
29,154 | em | In this paper we aim to improve existing empirical exchange rate models by
accounting for uncertainty with respect to the underlying structural
representation. Within a flexible Bayesian non-linear time series framework,
our modeling approach assumes that different regimes are characterized by
commonly used structural exchange rate models, with their evolution being
driven by a Markov process. We assume a time-varying transition probability
matrix with transition probabilities depending on a measure of the monetary
policy stance of the central bank at the home and foreign country. We apply
this model to a set of eight exchange rates against the US dollar. In a
forecasting exercise, we show that model evidence varies over time and a model
approach that takes this empirical evidence seriously yields improvements in
accuracy of density forecasts for most currency pairs considered. | Model instability in predictive exchange rate regressions | 2018-11-21 19:40:00 | Niko Hauzenberger, Florian Huber | http://arxiv.org/abs/1811.08818v2, http://arxiv.org/pdf/1811.08818v2 | econ.EM |
29,155 | em | Volatilities, in high-dimensional panels of economic time series with a
dynamic factor structure on the levels or returns, typically also admit a
dynamic factor decomposition. We consider a two-stage dynamic factor model
method recovering the common and idiosyncratic components of both levels and
log-volatilities. Specifically, in a first estimation step, we extract the
common and idiosyncratic shocks for the levels, from which a log-volatility
proxy is computed. In a second step, we estimate a dynamic factor model, which
is equivalent to a multiplicative factor structure for volatilities, for the
log-volatility panel. By exploiting this two-stage factor approach, we build
one-step-ahead conditional prediction intervals for large $n \times T$ panels
of returns. Those intervals are based on empirical quantiles, not on
conditional variances; they can be either equal- or unequal- tailed. We provide
uniform consistency and consistency rates results for the proposed estimators
as both $n$ and $T$ tend to infinity. We study the finite-sample properties of
our estimators by means of Monte Carlo simulations. Finally, we apply our
methodology to a panel of asset returns belonging to the S&P100 index in order
to compute one-step-ahead conditional prediction intervals for the period
2006-2013. A comparison with the componentwise GARCH benchmark (which does not
take advantage of cross-sectional information) demonstrates the superiority of
our approach, which is genuinely multivariate (and high-dimensional),
nonparametric, and model-free. | Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals | 2018-11-25 19:06:08 | Matteo Barigozzi, Marc Hallin | http://dx.doi.org/10.1016/j.jeconom.2020.01.003, http://arxiv.org/abs/1811.10045v2, http://arxiv.org/pdf/1811.10045v2 | econ.EM |
29,156 | em | This paper studies model selection in semiparametric econometric models. It
develops a consistent series-based model selection procedure based on a
Bayesian Information Criterion (BIC) type criterion to select between several
classes of models. The procedure selects a model by minimizing the
semiparametric Lagrange Multiplier (LM) type test statistic from Korolev (2018)
but additionally rewards simpler models. The paper also develops consistent
upward testing (UT) and downward testing (DT) procedures based on the
semiparametric LM type specification test. The proposed semiparametric LM-BIC
and UT procedures demonstrate good performance in simulations. To illustrate
the use of these semiparametric model selection procedures, I apply them to the
parametric and semiparametric gasoline demand specifications from Yatchew and
No (2001). The LM-BIC procedure selects the semiparametric specification that
is nonparametric in age but parametric in all other variables, which is in line
with the conclusions in Yatchew and No (2001). The results of the UT and DT
procedures heavily depend on the choice of tuning parameters and assumptions
about the model errors. | LM-BIC Model Selection in Semiparametric Models | 2018-11-26 23:29:18 | Ivan Korolev | http://arxiv.org/abs/1811.10676v1, http://arxiv.org/pdf/1811.10676v1 | econ.EM |
29,157 | em | This paper studies a fixed-design residual bootstrap method for the two-step
estimator of Francq and Zako\"ian (2015) associated with the conditional
Expected Shortfall. For a general class of volatility models the bootstrap is
shown to be asymptotically valid under the conditions imposed by Beutner et al.
(2018). A simulation study is conducted revealing that the average coverage
rates are satisfactory for most settings considered. There is no clear evidence
to have a preference for any of the three proposed bootstrap intervals. This
contrasts results in Beutner et al. (2018) for the VaR, for which the
reversed-tails interval has a superior performance. | A Residual Bootstrap for Conditional Expected Shortfall | 2018-11-27 01:03:46 | Alexander Heinemann, Sean Telg | http://arxiv.org/abs/1811.11557v1, http://arxiv.org/pdf/1811.11557v1 | econ.EM |
29,158 | em | We provide a complete asymptotic distribution theory for clustered data with
a large number of independent groups, generalizing the classic laws of large
numbers, uniform laws, central limit theory, and clustered covariance matrix
estimation. Our theory allows for clustered observations with heterogeneous and
unbounded cluster sizes. Our conditions cleanly nest the classical results for
i.n.i.d. observations, in the sense that our conditions specialize to the
classical conditions under independent sampling. We use this theory to develop
a full asymptotic distribution theory for estimation based on linear
least-squares, 2SLS, nonlinear MLE, and nonlinear GMM. | Asymptotic Theory for Clustered Samples | 2019-02-05 02:46:04 | Bruce E. Hansen, Seojeong Lee | http://arxiv.org/abs/1902.01497v1, http://arxiv.org/pdf/1902.01497v1 | econ.EM |
29,159 | em | In this paper we propose a general framework to analyze prediction in time
series models and show how a wide class of popular time series models satisfies
this framework. We postulate a set of high-level assumptions, and formally
verify these assumptions for the aforementioned time series models. Our
framework coincides with that of Beutner et al. (2019, arXiv:1710.00643) who
establish the validity of conditional confidence intervals for predictions made
in this framework. The current paper therefore complements the results in
Beutner et al. (2019, arXiv:1710.00643) by providing practically relevant
applications of their theory. | A General Framework for Prediction in Time Series Models | 2019-02-05 13:06:04 | Eric Beutner, Alexander Heinemann, Stephan Smeekes | http://arxiv.org/abs/1902.01622v1, http://arxiv.org/pdf/1902.01622v1 | econ.EM |
29,162 | em | The identification of the network effect is based on either group size
variation, the structure of the network or the relative position in the
network. I provide easy-to-verify necessary conditions for identification of
undirected network models based on the number of distinct eigenvalues of the
adjacency matrix. Identification of network effects is possible; although in
many empirical situations existing identification strategies may require the
use of many instruments or instruments that could be strongly correlated with
each other. The use of highly correlated instruments or many instruments may
lead to weak identification or many instruments bias. This paper proposes
regularized versions of the two-stage least squares (2SLS) estimators as a
solution to these problems. The proposed estimators are consistent and
asymptotically normal. A Monte Carlo study illustrates the properties of the
regularized estimators. An empirical application, assessing a local government
tax competition model, shows the empirical relevance of using regularization
methods. | Weak Identification and Estimation of Social Interaction Models | 2019-02-16 22:36:11 | Guy Tchuente | http://arxiv.org/abs/1902.06143v1, http://arxiv.org/pdf/1902.06143v1 | econ.EM |
29,163 | em | This paper is concerned with learning decision makers' preferences using data
on observed choices from a finite set of risky alternatives. We propose a
discrete choice model with unobserved heterogeneity in consideration sets and
in standard risk aversion. We obtain sufficient conditions for the model's
semi-nonparametric point identification, including in cases where consideration
depends on preferences and on some of the exogenous variables. Our method
yields an estimator that is easy to compute and is applicable in markets with
large choice sets. We illustrate its properties using a dataset on property
insurance purchases. | Discrete Choice under Risk with Limited Consideration | 2019-02-18 19:05:32 | Levon Barseghyan, Francesca Molinari, Matthew Thirkettle | http://arxiv.org/abs/1902.06629v3, http://arxiv.org/pdf/1902.06629v3 | econ.EM |
29,164 | em | The synthetic control method is often used in treatment effect estimation
with panel data where only a few units are treated and a small number of
post-treatment periods are available. Current estimation and inference
procedures for synthetic control methods do not allow for the existence of
spillover effects, which are plausible in many applications. In this paper, we
consider estimation and inference for synthetic control methods, allowing for
spillover effects. We propose estimators for both direct treatment effects and
spillover effects and show they are asymptotically unbiased. In addition, we
propose an inferential procedure and show it is asymptotically unbiased. Our
estimation and inference procedure applies to cases with multiple treated units
or periods, and where the underlying factor model is either stationary or
cointegrated. In simulations, we confirm that the presence of spillovers
renders current methods biased and have distorted sizes, whereas our methods
yield properly sized tests and retain reasonable power. We apply our method to
a classic empirical example that investigates the effect of California's
tobacco control program as in Abadie et al. (2010) and find evidence of
spillovers. | Estimation and Inference for Synthetic Control Methods with Spillover Effects | 2019-02-20 02:19:26 | Jianfei Cao, Connor Dowd | http://arxiv.org/abs/1902.07343v2, http://arxiv.org/pdf/1902.07343v2 | econ.EM |
29,165 | em | I show how to reveal ambiguity-sensitive preferences over a single natural
event. In the proposed elicitation mechanism, agents mix binarized bets on the
uncertain event and its complement under varying betting odds. The mechanism
identifies the interval of relevant probabilities for maxmin and maxmax
preferences. For variational preferences and smooth second-order preferences,
the mechanism reveals inner bounds, that are sharp under high stakes. For small
stakes, mixing under second-order preferences is dominated by the variance of
the second-order distribution. Additionally, the mechanism can distinguish
extreme ambiguity aversion as in maxmin preferences and moderate ambiguity
aversion as in variational or smooth second-order preferences. An experimental
implementation suggests that participants perceive almost as much ambiguity for
the stock index and actions of other participants as for the Ellsberg urn,
indicating the importance of ambiguity in real-world decision-making. | Eliciting ambiguity with mixing bets | 2019-02-20 11:19:21 | Patrick Schmidt | http://arxiv.org/abs/1902.07447v4, http://arxiv.org/pdf/1902.07447v4 | econ.EM |
29,166 | em | Ordered probit and logit models have been frequently used to estimate the
mean ranking of happiness outcomes (and other ordinal data) across groups.
However, it has been recently highlighted that such ranking may not be
identified in most happiness applications. We suggest researchers focus on
median comparison instead of the mean. This is because the median rank can be
identified even if the mean rank is not. Furthermore, median ranks in probit
and logit models can be readily estimated using standard statistical softwares.
The median ranking, as well as ranking for other quantiles, can also be
estimated semiparametrically and we provide a new constrained mixed integer
optimization procedure for implementation. We apply it to estimate a happiness
equation using General Social Survey data of the US. | Robust Ranking of Happiness Outcomes: A Median Regression Perspective | 2019-02-20 21:50:07 | Le-Yu Chen, Ekaterina Oparina, Nattavudh Powdthavee, Sorawoot Srisuma | http://arxiv.org/abs/1902.07696v3, http://arxiv.org/pdf/1902.07696v3 | econ.EM |
29,167 | em | We bound features of counterfactual choices in the nonparametric random
utility model of demand, i.e. if observable choices are repeated cross-sections
and one allows for unrestricted, unobserved heterogeneity. In this setting,
tight bounds are developed on counterfactual discrete choice probabilities and
on the expectation and c.d.f. of (functionals of) counterfactual stochastic
demand. | Nonparametric Counterfactuals in Random Utility Models | 2019-02-22 06:07:40 | Yuichi Kitamura, Jörg Stoye | http://arxiv.org/abs/1902.08350v2, http://arxiv.org/pdf/1902.08350v2 | econ.EM |
29,168 | em | We propose a counterfactual Kaplan-Meier estimator that incorporates
exogenous covariates and unobserved heterogeneity of unrestricted
dimensionality in duration models with random censoring. Under some regularity
conditions, we establish the joint weak convergence of the proposed
counterfactual estimator and the unconditional Kaplan-Meier (1958) estimator.
Applying the functional delta method, we make inference on the cumulative
hazard policy effect, that is, the change of duration dependence in response to
a counterfactual policy. We also evaluate the finite sample performance of the
proposed counterfactual estimation method in a Monte Carlo study. | Counterfactual Inference in Duration Models with Random Censoring | 2019-02-22 17:17:05 | Jiun-Hua Su | http://arxiv.org/abs/1902.08502v1, http://arxiv.org/pdf/1902.08502v1 | econ.EM |
29,169 | em | We show that when a high-dimensional data matrix is the sum of a low-rank
matrix and a random error matrix with independent entries, the low-rank
component can be consistently estimated by solving a convex minimization
problem. We develop a new theoretical argument to establish consistency without
assuming sparsity or the existence of any moments of the error matrix, so that
fat-tailed continuous random errors such as Cauchy are allowed. The results are
illustrated by simulations. | Robust Principal Component Analysis with Non-Sparse Errors | 2019-02-23 07:55:29 | Jushan Bai, Junlong Feng | http://arxiv.org/abs/1902.08735v2, http://arxiv.org/pdf/1902.08735v2 | econ.EM |
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