Journal of Economic Literature (JEL) Code(s)
We discuss some conceptual and practical issues that arise from the presence of global energy balance eﬀects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The ﬁndings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from ﬁxed eﬀect heterogeneity across individual station level observations. Diﬀerence GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly from an econometric perspective and importantly for global policy analysis, it is shown that despite the substantial diﬀerences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO₂) are robust to the estimation method employed and to the speciﬁc nature of the trending mechanism in global temperature, radiation, and CO₂.
Phillips, Peter C.B., "Dynamic Panel Modeling of Climate Change" (2018). Cowles Foundation Discussion Papers. 115.