Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects

Guowei Cui; Milda Norkute; Vasilis Sarafidis; Takashi Yamagata

doi:10.1093/ectj/utab029

Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects

Guowei Cui, Milda Norkute, Vasilis Sarafidis, Takashi Yamagata

Economics

Research output: Contribution to journal › Article › peer-review

Abstract

This paper analyses the instrumental variables (IV) approach put forward by \citet{NorkuteEtal20}, in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the $\sqrt{NT}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, whilst (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.

Original language	English
Pages (from-to)	340–361
Number of pages	22
Journal	Econometrics Journal
Volume	25
Issue number	2
Early online date	19 Oct 2021
DOIs	https://doi.org/10.1093/ectj/utab029
Publication status	Published - 1 May 2022

Bibliographical note

© The Author(s) 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

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10.1093/ectj/utab029

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Cite this

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title = "Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects",

abstract = "This paper analyses the instrumental variables (IV) approach put forward by \citet{NorkuteEtal20}, in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the $\sqrt{NT}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, whilst (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.",

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AU - Sarafidis, Vasilis

AU - Yamagata, Takashi

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AB - This paper analyses the instrumental variables (IV) approach put forward by \citet{NorkuteEtal20}, in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the $\sqrt{NT}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, whilst (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.

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