Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models

The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods.

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Nonparametric panel data regression with parametric cross-sectional dependence

Econometrics Journal ◽

10.1093/ectj/utab016 ◽

2021 ◽

Author(s):

Alexandra Soberon ◽

Juan M Rodriguez-Poo ◽

Peter M Robinson

Keyword(s):

Panel Data ◽

Monte Carlo Study ◽

Generalized Least Squares ◽

Linear Estimator ◽

Dependence Structure ◽

Finite Sample ◽

Cross Sectional ◽

Panel Data Regression ◽

Local Linear Estimator ◽

Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

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What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

Sociological Methodology ◽

10.1177/00811750211046307 ◽

2021 ◽

pp. 008117502110463

Author(s):

Ryan P. Thombs ◽

Xiaorui Huang ◽

Jared Berry Fitzgerald

Keyword(s):

Fixed Effects ◽

Directional Asymmetry ◽

Cross Sectional ◽

Monte Carlo Experiments ◽

Long Run ◽

Large N ◽

Fixed Effects Estimator ◽

Cross Sectional Dependence ◽

Using Data ◽

Heterogeneous Panel

Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large- N, large- T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.

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