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首页> 外文期刊>Econometrics >Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments
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Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments

机译：存在内生回归变量和许多仪器的空间自回归模型的广义空间两阶段最小二乘估计

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This paper studies the generalized spatial two stage least squares (GS2SLS) estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE) that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
机译：当存在具有许多有效工具的内生回归变量时，本文研究具有自回归干扰的空间自回归模型的广义空间两阶段最小二乘估计。使用许多工具可以渐近地提高估计器的效率，但是在有限的样本中偏差可能很大，从而导致推论不准确。我们考虑了以下情况：工具K的数量随样本数量的增加而增加，但增速慢于样本数量，并得出了近似均方误差（MSE），该均方根误差解释了偏差和方差之间的权衡。 GS2SLS估算器和经过偏差校正的GS2SLS估算器。最佳K选择的标准函数可以基于近似MSE。提供了蒙特卡洛实验，以证明我们选择K的过程的性能。

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