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空间相关数据模型的Moran’s I lasso估计AI 解读

Summary This paper proposes a lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran’s I lasso (Mi-lasso) to solve the eigenvector spatial filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted spatially correlated terms in a classical linear regression framework, thus eliminating the need for the researcher to…

存在地点特定不可观测局部效应的样本选择模型：空间差分方法AI 解读

Summary This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from ‘site-specific’ unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator is consistent and asymptotically normal. A Monte Carlo experiment…

多工具变量下的正则化有限信息最大似然估计AI 解读

The use of many moment conditions improves the asymptotic efficiency of the instrumental variables estimators. However, in finite samples, the inclusion of an excessive number of moments increases the bias. To solve this problem, we propose regularized versions of the limited information maximum likelihood (LIML) based on three different regularizations: Tikhonov, Landweber–Fridman, and principal components. Our estimators are consistent and asymptotically normal under…