Jin et al. (2020) proposed an efficient, distribution-free least squares estimation method that utilizes the eigendecomposition of a weight matrix in a dynamic space–time pooled panel data model. Their three-step approach is very powerful compared to the well-known instrumental variable techniques. Unfortunately, for short panels, their method can lead to biased estimates of the autoregressive time dependence parameter and the spatio-temporal diffusion parameter, even when using their bias-corrected estimator. We propose a bias correction method inspired from Bun and Carree (2005, 2006) of the Jin et al. (2020) procedure. We also extend their eigendecomposition-based least squares procedure to the random effects model, the fixed effects model, the Mundlak-type and Chamberlain-type correlated random effects models, the Hausman–Taylor model and the common correlated effects model. Extensive Monte Carlo experiments show the good finite sample properties of the proposed estimators. An application on the link between pollution and economic activities, using a dynamic space–time STIRPAT model with common correlated effects on a panel of 81 countries over 1991–2015, shows the relevance of this approach. It underlines the importance of human activities in the pollution growth while reforestation is one of the most important levers to reduce the CO${}_2$ emissions per capita.

金等人。(2020)提出了一种高效的、无分布的最小二乘估计方法，该方法利用动态时空池面板数据模型中权重矩阵的特征分解。与众所周知的工具变量技术相比，他们的三步方法非常强大。不幸的是，对于短面板，即使使用偏差校正估计器，他们的方法也可能导致自回归时间依赖性参数和时空扩散参数的估计有偏差。我们提出了一种受 Jin 等人的 Bun 和 Carree (2005, 2006) 启发的偏差校正方法。（2020）程序。我们还将他们基于特征分解的最小二乘过程扩展到随机效应模型、固定效应模型、Mundlak 型和 Chamberlain 型相关随机效应模型、Hausman-Taylor 模型和常见相关效应模型。广泛的蒙特卡罗实验表明所提出的估计器具有良好的有限样本特性。一项关于污染与经济活动之间联系的应用，使用动态时空 STIRPAT 模型，对 1991 年至 2015 年期间 81 个国家的小组进行了共同相关效应，显示了该方法的相关性。它强调了人类活动在污染增长中的重要性，而重新造林是减少二氧化碳排放的最重要杠杆之一${}_2$人均排放量。