Published by De Gruyter

Volume 10 Issue 1

January 2021

Issue of Journal of Econometric Methods

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Contents
Journal Overview

Publicly Available January 27, 2021

Frontmatter

Page range: i-ii

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Research Articles

March 27, 2020

Identification of Non-Equilibrium Beliefs in Games of Incomplete Information Using Experimental Data

Victor Aguirregabiria, Erhao Xie

Article number: 20190029

Abstract

This paper studies the identification of players’ preferences and beliefs in discrete choice games using experimental data. The experiment comprises a set of games that differ in their matrices of monetary payoffs. The researcher is interested in the identification of preferences (utility of money) and beliefs on the opponents’ expected behavior, without imposing equilibrium restrictions or parametric assumptions on utility and belief functions. We show that the hypothesis of unbiased/rational beliefs is testable as long as the set of games in the experiment imply variation in monetary payoffs of other players, keeping the own monetary payoff constant. We present conditions for the full identification of utility and belief functions at the individual level – without restrictions on players’ heterogeneity in preferences or beliefs. We apply our method to data from two experiments: a matching pennies game, and a public good game.

August 31, 2020

Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility

Tae-Hwy Lee, Millie Yi Mao, Aman Ullah

Page range: 1-19

Abstract

Based on the maximum entropy (ME) method, we introduce an information theoretic approach to estimating conditional moment functions with incorporating a theoretical constraint implied from the consumption-based capital asset pricing model (CCAPM). Using the ME conditional mean/variance functions obtained from the ME density, we analyze the relationship between asset returns and consumption growth under the theoretical constraint of the CCAPM. We evaluate the predictability of asset return using consumption growth through in-sample estimation and out-of-sample prediction in the ME mean regression function. We also examine the ME variance regression function for the asset return volatility as a function of the consumption growth. Our findings suggest that incorporating the CCAPM constraint can capture the nonlinear predictability of asset returns in mean especially in tails, and that the consumption growth has an effect on reducing stock return volatility, indicating the counter-cyclical variation of stock market volatility.

October 7, 2020

Time–Frequency Regression

Yoshito Funashima

Page range: 21-32

Abstract

Wavelet analysis is widely used to trace macroeconomic and financial phenomena in time–frequency domains. However, existing wavelet measures diverge from conventional regression estimators. Furthermore, a direct comparison between wavelet and traditional regression analyses is difficult. In this study, we modify the partial wavelet gain to provide an estimator that corresponds to the ordinary least squares estimator at each point of the time–frequency space. We argue that from the viewpoint of practical applications, the modified partial wavelet gain is suitable for contemporary regressions across time and frequencies, whereas the original partial wavelet gain is suitable for evaluating an aggregate relationship of contemporaneous and lead-lag relationships.

October 7, 2020

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

Hiroyuki Kawakatsu

Page range: 33-52

Abstract

This paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

October 29, 2020

Identification of Seasonal Effects in Impulse Responses Using Score-Driven Multivariate Location Models

Szabolcs Blazsek, Alvaro Escribano, Adrian Licht

Page range: 53-66

Abstract

For policy decisions, capturing seasonal effects in impulse responses are important for the correct specification of dynamic models that measure interaction effects for policy-relevant macroeconomic variables. In this paper, a new multivariate method is suggested, which uses the score-driven quasi-vector autoregressive (QVAR) model, to capture seasonal effects in impulse response functions (IRFs). The nonlinear QVAR-based method is compared with the existing linear VAR-based method. The following technical aspects of the new method are presented: (i) mathematical formulation of QVAR; (ii) first-order representation and infinite vector moving average, VMA (∞), representation of QVAR; (iii) IRF of QVAR; (iv) statistical inference of QVAR and conditions of consistency and asymptotic normality of the estimates. Control data are used for the period of 1987:Q1 to 2013:Q2, from the following policy-relevant macroeconomic variables: crude oil real price, United States (US) inflation rate, and US real gross domestic product (GDP). A graphical representation of seasonal effects among variables is provided, by using the IRF. According to the estimation results, annual seasonal effects are almost undetected by using the existing linear VAR tool, but those effects are detected by using the new QVAR tool.

December 21, 2020

Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection

Annabelle Doerr, Anthony Strittmatter

Page range: 67-88

Abstract

We study the identification of channels of policy reforms with multiple treatments and different types of selection for each treatment. We disentangle reform effects into policy effects, selection effects, and time effects under the assumption of conditional independence, common trends, and an additional exclusion restriction on the non-treated. Furthermore, we show the identification of direct- and indirect policy effects after imposing additional sequential conditional independence assumptions on mediating variables. We illustrate the approach using the German reform of the allocation system of vocational training for unemployed persons. The reform changed the allocation of training from a mandatory system to a voluntary voucher system. Simultaneously, the selection criteria for participants changed, and the reform altered the composition of course types. We consider the course composition as a mediator of the policy reform. We show that the empirical evidence from previous studies reverses when considering the course composition. This has important implications for policy conclusions.

Teaching Corner

October 16, 2020

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

Danielle V. Handel, Anson T. Y. Ho, Kim P. Huynh, David T. Jacho-Chávez, Carson H. Rea

Page range: 89-102

Abstract

This paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

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