Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models

Haruo Iwakura () and Ryo Okui
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Haruo Iwakura: Graduate School of Economics, Kyoto University

No 887, KIER Working Papers from Kyoto University, Institute of Economic Research

Abstract: This paper studies the asymptotic efficiency in factor models with serially correlated errors and dynamic panel data models with interactive effects. We derive the efficiency bound for the estimation of factors, factor loadings and common parameters that describe the dynamic structure. We use double asymptotics under which both the cross-sectional sample size and the length of the time series tend to in nity. The results show that the efficiency bound for factors is not affected by the presence of unknown factor loadings and common parameters, and analogous results hold for the bounds for factor loadings and common parameters. The efficiency bound is derived by using an in nite-dimensional con- volution theorem. Perturbation to the in nite-dimensional parameters, which consists in an important step of the derivation of the efficiency bound, is nontrivial and is discussed in detail.

Keywords: asymptotic efficiency; convolution theorem; double asymptotics; dynamic panel data model; factor model; interactive effects. (search for similar items in EconPapers)
JEL-codes: C13 C23 (search for similar items in EconPapers)
Pages: 59pages
Date: 2014-02
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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