Published since January 1, 2011

Journal of Econometric Methods

ISSN: 2156-6674

Edited by: Tong Li, Zhongjun Qu

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About this journal

Objective
The Journal of Econometric Methods welcomes submissions in theoretical and applied econometrics of direct relevance to empirical economics research. The journal aims to bridge the widening gap between econometric research and empirical practice. We aim to publish papers from top scholars in econometrics, but submissions must (i) consider a topic of broad interest to practitioners and (ii) be written in a style that is targeted at practitioners. Subject to these requirements, the journal will consider submissions in all areas of econometrics. We do not consider submissions that are application-specific. While econometric methodology should be thoroughly illustrated with empirical data, such methodology should be useful above and beyond the specific application considered.

Topics

Estimation and testing
Cross-sectional, panel-data, and time-series methodologies
Quantile regression
Nonparametric estimation
Bootstrap inference
Teaching econometrics

Article formats
Research article submissions should make a significant contribution to the existing econometrics literature. This contribution can consist of new methodology, new theoretical results, new computational methods, comparison of existing methodologies, etc. Upon acceptance of their paper, authors are required to provide any data and computer programs so that readers can replicate results and utilize the econometric methods for their own research. Papers will not be published until these materials have been made available.

In addition to traditional research articles, the journal will also publish contributions to its “Practitioners’ corner” and “Teaching corner” sections:

Practitioners’ corner: This section publish “how-to” and survey papers for practitioners. Papers in the section can include: detailed treatments of specific econometric methods not covered extensively (or well) elsewhere, surveys that bring empirical researchers up-to-date on quickly developing research areas, etc.

Teaching corner: This section publish papers that are directly useful for econometrics instructors at the undergraduate or graduate level. Papers in the section can provide datasets (with completely worked-through analysis) that help instructors to illustrate specific models and/or methodologies. Papers in this section can also provide other tools for instructors, including novel suggestions for presenting certain material, simulation designs, etc.

> Information on submission process

Your Benefits

Your benefits:

Theoretical and applied approach to econometrics
Direct relevance to empirical economics research
Imperative topics of broad interest to practitioners
Up-to-date empirical tools for practitioners and instructors
Cutting-edge methods applicable to a wide range of empirical problems
Detailed methodologies that can be easily implemented
Rigorous peer-review

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Supplementary Materials

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Volume 12 Issue 1

January 2023

Publicly Available January 26, 2023

Frontmatter

Page range: i-ii

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Research Articles

November 29, 2022

Empirical Framework for Two-Player Repeated Games with Random States

Arkadiusz Szydłowski

Page range: 1-31

Abstract

We provide methods for the empirical analysis of a class of two-player repeated games with i.i.d. shocks, allowing for non-Markovian strategies. The number of possible equilibria in these games is large and, usually, theory is silent about which equilibrium will be chosen in practice. Thus, our method remains agnostic about selection among these multiple equilibria, which leads to partial identification of the parameters of the game. We propose a profiled likelihood criterion for building confidence sets for the structural parameters of the game and derive an easily computable upper bound on the critical value. We demonstrate good finite-sample performance of our procedure using a simulation study. We illustrate the usefulness of our method by studying the effect of repealing the Wright Amendment on entry and exit into Dallas airline markets and find that the static game approach overestimates the negative effect of the law on entry into these markets.

November 17, 2021

The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

Do Won Kwak, Robert S. Martin, Jeffrey M. Wooldridge

Page range: 33-56

Abstract

We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

December 3, 2021

Density Forecast of Financial Returns Using Decomposition and Maximum Entropy

Tae-Hwy Lee, He Wang, Zhou Xi, Ru Zhang

Page range: 57-83

Abstract

We consider a multiplicative decomposition of the financial returns to improve the density forecasts of financial returns. The multiplicative decomposition is based on the identity that financial return is the product of its absolute value and its sign. Advantages of modeling the two components are discussed. To reduce the effect of the estimation error due to the multiplicative decomposition in estimation of the density forecast model, we impose a moment constraint that the conditional mean forecast is set to match with the sample mean. Imposing such a moment constraint operates a shrinkage and tilts the density forecast of the decomposition model to produce the improved maximum entropy density forecast. An empirical application to forecasting density of the daily stock returns demonstrates the benefits of using the decomposition and imposing the moment constraint to obtain the improved density forecast. We evaluate the density forecast by comparing the logarithmic score (LS), the quantile score (QS), and the continuous ranked probability score (CRPS). We contribute to the literature on the density forecast and the decomposition models by showing that the density forecast of the decomposition model can be improved by imposing a sensible constraint in the maximum entropy framework.

April 14, 2022

On the Implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters

Yong Cai, Ivan A. Canay, Deborah Kim, Azeem M. Shaikh

Page range: 85-103

Abstract

This paper provides a user’s guide to the general theory of approximate randomization tests developed in Canay, Romano, and Shaikh (2017a. “Randomization Tests under an Approximate Symmetry Assumption.” Econometrica 85 (3): 1013–30) 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: one to a linear regression with clustered data based on Meng, Qian, and Yared (2015. “The Institutional Causes of china’s Great Famine, 1959–1961.” The Review of Economic Studies 82 (4): 1568–611) and a second to a linear regression with temporally dependent data based on Munyo and Rossi (2015. “First-day Criminal Recidivism.” Journal of Public Economics 124: 81–90). The companion R and Stata packages facilitate the implementation of the methodology and the replication of the empirical exercises.

Publicly Available August 19, 2022

Quantile Difference in Differences with Time-Varying Qualification in Panel Data

Karim Nchare, Ryo Makioka

Page range: 105-116

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Abstract

This paper investigates the identification and estimation of the quantile treatment effect in a difference in differences (DID) setting when treatment is provided only to qualified individuals at a certain point in time and the qualification is time-varying. The time-varying qualification may affect an outcome variable even when the actual effect of treatment is zero. We show how to account for this “movers effect” bias and propose the quantile treatment effect on “in-stayers” that are qualified both before and after the treatment. The estimate is identified under three main assumptions: (i) panel data availability, (ii) a distributional common trend assumption conditional on covariates, and (iii) a copula stability assumption. We then apply our method to estimate the effects of an increase in the benefits of the Supplemental Nutrition Assistance Program (SNAP) on recipients’ food expenditure shares. The results show significant heterogeneity and highlight the importance of accounting for time-varying qualification.

June 23, 2022

A Random Forest-based Approach to Combining and Ranking Seasonality Tests

Daniel Ollech, Karsten Webel

Page range: 117-130

Abstract

Virtually every seasonal adjustment software includes an ensemble of tests for assessing whether a given time series is in fact seasonal and hence a candidate for seasonal adjustment. However, such tests are certain to produce either agreeing or conflicting results, raising the questions how to identify the most accurate tests and how to aggregate the results in the latter case. We suggest a novel random forest-based approach to answer these questions. We simulate seasonal and non-seasonal ARIMA processes that are representative of the macroeconomic time series analysed regularly by the Bundesbank. Treating the time series’ seasonal status as a classification problem, we use the p -values of the seasonality tests implemented in the seasonal adjustment software JDemetra+ as predictors to train conditional random forests on the simulated data. We show that this aggregation approach avoids the size distortions of the JDemetra+ tests without sacrificing too much power compared to the most powerful test. We also find that the modified QS and Friedman tests are the most accurate ones in the considered ensemble.

Teaching Corner

Open Access October 5, 2022

On the Use of the Helmert Transformation, and its Applications in Panel Data Econometrics

Gueorgui I. Kolev, Helmuts Āzacis

Page range: 131-138

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Abstract

We revisit the Helmert transformation, and provide a useful and simple derivation of the joint distribution of the sample mean and the sample variance in samples from independently and identically distributed normal random variables. Our derivation is distinguished by concreteness, very little abstractness, and should be appealing to beginning students of statistics, and to both beginning and advanced students of econometrics. We also highlight one fruitful application of the Helmert transformation in panel data econometrics. The Helmert transformation can be used to eliminate the fixed effects in the estimation of fixed effects models, and we briefly review this application of the transformation in the panel data context.

Practitioner's Corner

March 14, 2022

Linear Rescaling to Accurately Interpret Logarithms

Nick Huntington-Klein

Page range: 139-147

Abstract

The standard approximation of a natural logarithm in statistical analysis interprets a linear change of p in ln( X ) as a (1 + p ) proportional change in X , which is only accurate for small values of p . I suggest base-(1 + p ) logarithms, where p is chosen ahead of time. A one-unit change in log 1 + p ( X ) is exactly equivalent to a (1 + p ) proportional change in X . This avoids an approximation applied too broadly, makes exact interpretation easier and less error-prone, improves approximation quality when approximations are used, makes the change of interest a one-log-unit change like other regression variables, and reduces error from the use of log(1 + X ).

CiteScore	2.1
Mathematical Citation Quotient	0.07
SCImago Journal Rank	0.427
Source Normalized Impact per Paper	0.622

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Editorial

Editors
Tong Li, Vanderbilt University, USA
Zhongjun Qu, Boston University, USA

Associate Editors
Stephane Bonhomme, University of Chicago, USA
Christian Brownlees, Pompeu Fabra University, Spain
Xiaohong Chen, Yale University, USA
Xu Cheng, Department of Economics, University of Pennsylvania, USA
Timothy Christensen, Department of Economics, New York University, USA
Sergio Firpo, INSPER, Brazil
Toru Kitagawa, Brown University, USA
Brendan Kline, University of Texas, Austin, USA
Laura Liu, Department of Economics, Indiana University, USA
Adam McCloskey, Department of Economics, University of Colorado at Boulder, USA
Anna Mikusheva, Massachusetts Institute of Technology, USA
Francesca Molinari, Cornell University, USA
Jose L. Montiel Olea, New York University, USA
Mikkel Plagborg-Moller, Department of Economics, Princeton University, USA
Demian Pouzo, University of California, Berkeley, USA
Adam Rosen, Duke University, USA
Yuya Sasaki, Vanderbilt University, USA
Martin Weidner, University College London, UK
Yoon-Jae Whang, Seoul National University, South Korea
Daniel Wilhelm, University College London, UK

(Deutsch)

Online ISSN: 2156-6674
Type: Journal
Language: English
Publisher: De Gruyter
First published: January 1, 2011
Publication Frequency: 1 Issue per Year
Audience: researchers, practitioners and instructors in the field of econometrics