The Econometrics Journal Current Issue

08 May 2019

Separating different individual effects in a panel data model

Amsler C, Schmidt P.

Summary
In this paper we consider a panel data model with individual effects that are arbitrarily correlated with the explanatory variables. The effects are composed as the sum of two different interpretable components, such as inefficiency versus heterogeneity in a production frontier setting, or ability versus socioeconomic background in an earnings function, or genetics versus environment in an epidemiological analysis. We wish to predict the two components separately. This is made possible by assuming that there are observables that are correlated with the first component but not with the second, and other observables that are correlated with the second component but not with the first. This can be true in terms of either simple correlations or partial correlations.
08 May 2019

A simple, graphical approach to comparing multiple treatments

Thompson B, Webb M.

Summary
We consider a graphical approach to comparing multiple treatments that allows users to easily infer differences between any treatment effect and zero, and between any pair of treatment effects. This approach makes use of a flexible, resampling-based procedure that asymptotically controls the familywise error rate (the probability of making one or more spurious inferences). We demonstrate the usefulness of this approach with three empirical examples.
19 Mar 2019

Inferential results for a new measure of inequality

Davydov Y, Greselin F.

Summary
In recent decades, substantial changes have been observed in the left and right tails of income distributions in countries like the USA, Germany, the UK, and France. These changes are a major concern for policy makers. Here, we derive inferential results for a new inequality index that is specifically designed for capturing such significant shifts. We propose two empirical estimators for the index and show that they are asymptotically equivalent. Afterward, we adopt one estimator and prove its consistency and asymptotic normality. Finally, we introduce an empirical estimator for its variance and provide conditions for its consistency. An analysis of real data from the Bank of Italy Survey of Income and Wealth is also presented on the basis of the obtained inferential results.
29 Jan 2019

Quantile coherency: A general measure for dependence between cyclical economic variables

Baruník J, Kley T.

Summary
In this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is natural for economic time series but remains invisible when only the traditional analysis is employed. We define estimators that capture the general dependence structure, provide a detailed analysis of their asymptotic properties, and discuss how to conduct inference for a general class of possibly nonlinear processes. In an empirical illustration we examine the dependence of bivariate stock market returns and shed new light on measurement of tail risk in financial markets. We also provide a modelling exercise to illustrate how applied researchers can benefit from using quantile coherency when assessing time series models.
29 Jan 2019

On the role of covariates in the synthetic control method

Botosaru I, Ferman B.

Summary
Abadie et al. (2010) derive bounds on the bias of the synthetic control estimator under a perfect balance assumption on both observed covariates and pre-treatment outcomes. In the absence of a perfect balance on covariates, we show that it is still possible to derive such bounds, albeit at the expense of relying on stronger assumptions about the effects of observed and unobserved covariates and of generating looser bounds. We also show that a perfect balance on pre-treatment outcomes does not generally imply an approximate balance for all covariates, even when they are all relevant. Our results have important implications for the implementation of the method.
29 Jan 2019

Testing collinearity of vector time series

McElroy T, Jach A.

Summary
We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing for rank reduction via assessing the positivity of these Schur complements, which are obtained from a nonparametric estimator of the spectral density. New asymptotic results for the test statistics are derived under the fixed bandwidth ratio paradigm; they diverge under the alternative, but under the null hypothesis of collinearity the test statistics converge to a non-standard limiting distribution. Subsampling is used to obtain the limiting null quantiles. A simulation study and an empirical illustration for 6-variate time series data are provided.