数学
估计员
自回归模型
广义矩量法
正规化(语言学)
应用数学
协方差矩阵
协方差
力矩(物理)
选型
维数(图论)
数学优化
计量经济学
统计
计算机科学
人工智能
物理
经典力学
纯数学
作者
Marine Carrasco,Ada Nayihouba
出处
期刊:Econometric Theory
[Cambridge University Press]
日期:2022-10-28
卷期号:40 (2): 360-418
被引量:2
标识
DOI:10.1017/s0266466622000469
摘要
In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. As a consequence, the generalized method of moments (GMM) estimator exhibits a large bias in small samples, especially when the autoregressive parameter is close to unity. To address this issue, we propose a regularized version of the one-step GMM estimator using three regularization schemes based on three different ways of inverting the covariance matrix of the instruments. Under double asymptotics, we show that our regularized estimators are consistent and asymptotically normal. These regularization schemes involve a tuning or regularization parameter which needs to be chosen. We derive a data-driven selection of this regularization parameter based on an approximation of the higher-order mean square error and show its optimality. As an empirical application, we estimate a model of income dynamics.
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