Estimating Ipat Models Using Panel Data

Abstract

This paper addresses econometric challenges arising in panel data analyses related to IPAT (environmental Impact of Population, Affluence and Technology) models. Panel data in this context is often characterized by a large-N and large-T structure. This poses specific econometric complexities due to nonstationarity and cross-sectional error correlation, affecting consistent estimation and valid inference. We provide a concise overview of these complications and how to deal with these with appropriate tests and models. Moreover, we apply these insights to empirical examples based on the IPAT identity, offering insights into the robustness of previous findings. Our results suggest that using standard panel techniques can lead to biased estimates, incorrect inference, and invalid model adequacy tests. This can potentially lead to flawed policy conclusions. We provide practical guidance to practitioners for navigating these econometric complexities.

Keywords: Nonstationary panel data, Cross-sectional dependence, Panel cointegration, GHG emissions, IPAT, Common correlated effects