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Deriving the Information Bounds for Nonlinear Panel Data Models with Fixed Effects

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

No 886, KIER Working Papers from Kyoto University, Institute of Economic Research

Abstract: This paper studies the asymptotic efficiency of estimates in nonlinear panel data models with fixed effects when both the cross-sectional sample size and the length of time series tend to infinity. The efficiency bounds for regular estimators are derived using the infinite-dimensional convolution theorem by van der Varrt and Wellner (1996). It should be noted that the number of fixed effects increases with the sample size, so they constitute an infinite-dimensional nuisance parameter. The presence of fixed effects makes our derivation of the efficiency bounds non-trivial, and the techniques to overcome the difficulties caused by fixed effects will be discussed in detail. Our results include the efficiency bounds for models containing unknown functions (for instance, a distribution function of error terms). We apply our results to show that the bias-corrected fixed effects estimator of Hahn and Newey (2004) is asymptotically efficient.

Keywords: asymptotic efficiency; convolution theorem; double asymptotics; nonlinear panel data model; fixed effects; interactive effects; factor structure; incidental parameters. (search for similar items in EconPapers)
JEL-codes: C13 C23 (search for similar items in EconPapers)
Pages: 26pages
Date: 2014-01
New Economics Papers: this item is included in nep-ecm
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