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 in this model 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 CO${}_2$) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO${}_2.$

中文翻译：

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气候变化的动态面板建模

我们讨论了在气候数据的动态面板回归中，全球能源平衡对站级调整机制的影响而引起的一些概念和实际问题。本文提供了渐近分析，观测数据计算和蒙特卡洛模拟，以评估各种估计方法的使用，包括早先研究中使用的标准动态面板回归和协整技术。这些发现揭示了动态面板回归参数在系统GMM估计中的巨大偏差，这是由各个站级观测值的固定效应异质性引起的。差异GMM和组内（WG）估计值几乎没有偏差，建议将WG估计值用于高度分解气候数据的动态面板回归的实际实施。${}_2$）对于采用的估算方法以及全球温度，辐射和CO中趋势机制的特定性质具有鲁棒性${}_2。$