Efficient estimation of heterogeneous coefficients in panel data models with common shock

Kunpeng Li and Lina Lu

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates efficient estimation of heterogeneous coefficients in panel data models with common shocks, which have been a particular focus of recent theoretical and empirical literature. We propose a new two-step method to estimate the heterogeneous coefficients. In the first step, the maximum likelihood (ML) method is first conducted to estimate the loadings and idiosyncratic variances. The second step estimates the heterogeneous coefficients by using the structural relations implied by the model and replacing the unknown parameters with their ML estimates. We establish the asymptotic theory of our estimator, including consistency, asymptotic representation, and limiting distribution. The two-step estimator is asymptotically efficient in the sense that it has the same limiting distribution as the infeasible generalized least squares (GLS) estimator. Intensive Monte Carlo simulations show that the proposed estimator performs robustly in a variety of data setups.

Keywords: Factor analysis; Block diagonal covariance; Panel data models; Common shocks; Maximum likelihood estimation, heterogeneous coefficients; Inferential theory (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
Date: 2014-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:59312

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