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

Ryan P. Thombs, Xiaorui Huang, Jared Berry Fitzgerald

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-29
Number of pages29
JournalSociological methodology
Volume52
Issue number1
DOIs
StatePublished - Feb 2022

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science

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