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

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Publication details

JournalJournal of Econometrics
DateAccepted/In press - 21 Jan 2020
DateE-pub ahead of print - 16 Jun 2020
DatePublished (current) - 1 Feb 2021
Issue number2
Number of pages31
Pages (from-to)416-446
Early online date16/06/20
Original languageEnglish


This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both the cross-sectional and time series dimensions, N and T respectively, are large. The main idea is to project out the common factors from the exogenous covariates of the model, and to construct instruments based on defactored covariates. For models with homogeneous slope coefficients, we propose a two-step IV estimator. In the first step, the model is estimated consistently by employing defactored covariates as instruments. In the second step, the entire model is defactored based on estimated factors extracted from the residuals of the first-step estimation, after which an IV regression is implemented using the same instruments as in step one. For models with heterogeneous slope coefficients, we propose a mean-group-type estimator, which involves the averaging of first-step IV estimates of cross-section-specific slopes. The proposed estimators do not need to seek for instrumental variables outside the model. Furthermore, these estimators are linear, and therefore computationally robust and inexpensive. Notably, they require no bias correction. We investigate the finite sample performances of the proposed estimators and associated statistical tests, and the results show that the estimators and the tests perform well even for small N and T.

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© 2020 Published by Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

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