Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change

Badi Baltagi, Georges Bresson, Anoop Chaturvedi () and Guy Lacroix

No 254, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach de-parts from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.

Keywords: Climate Change; Crop Yields; Dynamic Model; ε-Contamination; Panel Data; Robust Bayesian Estimator; Space-Time (search for similar items in EconPapers)
JEL-codes: C11 C23 C26 Q15 Q54 (search for similar items in EconPapers)
Pages: 34
Date: 2022-12
New Economics Papers: this item is included in nep-env
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https://surface.syr.edu/cpr/465/ (application/pdf)

Related works:
Working Paper: Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change (2023)
Working Paper: Robust Dynamic Space-Time Panel Data Models Using ?-Contamination: An Application to Crop Yields and Climate Change (2022)
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