Journal article
What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large- N , Large- T Data
Sociological methodology, v 52(1), pp 1-29
Feb 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
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–CO
2
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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Details
- Title
- What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large- N , Large- T Data
- Creators
- Ryan P. Thombs - Boston CollegeXiaorui Huang - Drexel University, SociologyJared Berry Fitzgerald - Boston College
- Publication Details
- Sociological methodology, v 52(1), pp 1-29
- Publisher
- Sage
- Number of pages
- 29
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Sociology
- Web of Science ID
- WOS:000923638400001
- Scopus ID
- 2-s2.0-85116099523
- Other Identifier
- 991021903707504721
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- Web of Science research areas
- Sociology