Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure

• Material Type: 文書・図像類
• Title: Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure
• Author/Editor: Norkutė, Milda; Sarafidis, Vasilis; Yamagata, Takashi; Cui, Guowei
• Author Heading: Norkutė, Milda; Sarafidis, Vasilis; Yamagata, Takashi; https://nrid.nii.ac.jp/nrid/1000020813231; Cui, Guowei
• Publication, Distribution, etc.: The Institute of Social and Economic Research, Osaka University
• Publication Date: 2019-04
• Publication Date (W3CDTF): 2019-04
• Periodical title: Institute of Social and Economic Research Discussion Papers
• No. or year of volume/issue: 1019
• Volume: 1019
• Pages: 1-C.12
• Text Language Code: eng
• Subject Heading: method of moments; dynamic panel data; cross-sectional dependence; factor model
• Target Audience: 一般
• Note (General): February 2018. Revised April 2019.; This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both cross-sectional and time series dimensions, N and T respectively, are large. Our approach initially projects out the common factors from the exogenous covariates of the model, and constructs instruments based on this defactored covariates. For models with homogeneous slope coefficients, we propose a two-step IV estimator: the first step IV estimator is obtained using the defactored covariates as instruments. In the second step, the entire model is defactored by the extracted factors from the residuals of the first step estimation and subsequently obtain the final IV estimator. For models with heterogeneous slope coefficients, we propose a mean-group type estimator, which is the cross-sectional average of first-step IV estimators of cross-section specific slopes. It is noteworthy that our estimators do not require us to seek for instrumental variables outside the model. Furthermore, our estimators are linear hence computationally robust and inexpensive. Moreover, they require no bias correction, and they are not subject to the small sample bias of least squares type estimators. The finite sample performances of the proposed estimators and associated statistical tests are investigated, and the results show that the estimators and the tests perform well even for small N and T.
• References (URI): https://www.iser.osaka-u.ac.jp/library/dp/2018/DP1019R.pdf; URI
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 大阪大学 : 大阪大学学術情報庫 桜華