This paper proposes simple tests of error cross-sectional dependence which are applicable to a variety of panel data models, including stationary and unit root dynamic heterogeneous panels with short
...T
and large
N
. The proposed tests are based on the average of pair-wise correlation coefficients of the OLS residuals from the individual regressions in the panel and can be used to test for cross-sectional dependence of any fixed order
p
, as well as the case where no a priori ordering of the cross-sectional units is assumed, referred to as
CD
(
p
)
and
CD
tests, respectively. Asymptotic distribution of these tests is derived and their power function analyzed under different alternatives. It is shown that these tests are correctly centred for fixed
N
and
T
and are robust to single or multiple breaks in the slope coefficients and/or error variances. The small sample properties of the tests are investigated and compared to the Lagrange multiplier test of Breusch and Pagan using Monte Carlo experiments. It is shown that the tests have the correct size in very small samples and satisfactory power, and, as predicted by the theory, they are quite robust to the presence of unit roots and structural breaks. The use of the
CD
test is illustrated by applying it to study the degree of dependence in per capita output innovations across countries within a given region and across countries in different regions. The results show significant evidence of cross-dependence in output innovations across many countries and regions in the World.
Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We ...overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.
We describe a major upgrade to the Stata (and R) rdrobust package, which provides a wide array of estimation, inference, and falsification methods for the analysis and interpretation of ...regression-discontinuity designs. The main new features of this upgraded version are as follows: i) covariate-adjusted bandwidth selection, point estimation, and robust bias-corrected inference, ii) cluster–robust bandwidth selection, point estimation, and robust bias-corrected inference, iii) weighted global polynomial fits and pointwise confidence bands in regression-discontinuity plots, and iv) several new bandwidth selection methods, including different bandwidths for control and treatment groups, coverage error-rate optimal bandwidths, and optimal bandwidths for fuzzy designs. In addition, the upgraded package has superior performance because of several numerical and implementation improvements. We also discuss issues of backward compatibility and provide a companion R package with the same syntax and capabilities.
Many students of statistics and econometrics express frustration with the way a problem known as “bad control” is treated in the traditional literature. The issue arises when the addition of a ...variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this “crash course” accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.
This paper introduces a quantile regression estimator for panel data (QRPD) with nonadditive fixed effects, maintaining the nonseparable disturbance term commonly associated with quantile estimation. ...QRPD estimates the impact of exogenous or endogenous treatment variables on the outcome distribution using “within” variation in the instruments for identification purposes. Most quantile panel data estimators include additive fixed effects which separates the disturbance term and assumes the parameters vary based only on the time-varying components of the disturbance term. QRPD produces consistent estimates for small
T
. I estimate the effect of the 2008 tax rebates on the short-term household consumption distribution.
It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are ...theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.
The Miriades guide (multidimensional analysis of the impact of research and its innovations on the development of societies in the South) is intended as a methodological tool for researchers working ...in partnership with countries in the South and wishing to analyse the societal impact of their work. It was conceived by taking into account the methods developed by other research organisations and adapted to the particularities of the IRD's partnership research approach by the methodological group set up within the IRD, then adjusted after a pilot phase of Miriades.
Le guide Miriades (analyse multidimensionnelle des impacts de la recherche et de ses innovations sur le développement des sociétés du Sud) se veut un outil méthodologique pour les chercheurs qui ...travaillent en partenariat avec les pays du Sud et souhaitent aborder l’analyse de l’impact sociétal de leurs travaux. Il a été construit en prenant en compte les méthodes développées par d’autres organismes de recherche et adapté aux particularités de la démarche de recherche en partenariat de l’IRD par le groupe méthodologique constitué au sein de l’IRD puis ajusté après une phase pilote de Miriades.
La guía Miriades (Análisis multidimensional del impacto de la investigación y sus innovaciones en el desarrollo de las sociedades del Sur) pretende ser una herramienta metodológica para los ...investigadores que trabajan en asociación con los países del Sur y que desean analizar el impacto social de sus trabajos. Ha sido elaborada teniendo en cuenta los métodos desarrollados por otros organismos de investigación y adaptada a las especificidades del enfoque de investigación en cooperación del IRD por el grupo metodológico creado por el IRD. Esta metodologia fue ajustada luego de la fase piloto del proyecto Miriades.
The canonical difference-in-differences (DD) estimator contains two time periods, ”pre” and ”post”, and two groups, ”treatment” and ”control”. Most DD applications, however, exploit variation across ...groups of units that receive treatment at different times. This paper shows that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data. A causal interpretation of two-way fixed effects DD estimates requires both a parallel trends assumption and treatment effects that are constant over time. I show how to decompose the difference between two specifications, and provide a new analysis of models that include time-varying controls.
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