What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data. Issue 1 (February 2022)

• Record Type: Journal Article
• Authors: Thombs, Ryan P.; Huang, Xiaorui; Fitzgerald, Jared Berry
• Is Part Of: Sociological methodology. Volume 52:Issue 1(2022)
• Page Start: 1
• Page End: 29
• Publication Date: 2022-02
• Subjects: panel data -- asymmetry -- heterogeneous panel estimators -- dynamic panel data -- cross-sectional dependence -- fixed effects; Sociology -- Methodology -- Periodicals; Sociology; 301.01
• DOI: 10.1177/00811750211046307
• Languages: English
• ISSNs: 0081-1750

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.