Estimation of Nonlinear Panel Models with Multiple Unobserved Effects
The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics
I propose a fixed effects expectation-maximization (EM) estimator that can be applied to a class of nonlinear panel data models with unobserved heterogeneity, which is modeled as individual effects and/or time effects. Of particular interest is the case of interactive effects, i.e. when the unobserved heterogeneity is modeled as a factor analytical structure. The estimator is obtained through a computationally simple, iterative two-step procedure, where the two steps have closed form solutions. I show that estimator is consistent in large panels and derive the asymptotic distribution for the case of the probit with interactive effects. I develop analytical bias corrections to deal with the incidental parameter problem. Monte Carlo experiments demonstrate that the proposed estimator has good finite-sample properties.
Keywords: Nonlinear panel; latent variables; interactive effects; factor error structure; EM algorithm; incidental parameters; bias correction (search for similar items in EconPapers)
JEL-codes: C13 C21 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Working Paper: Estimation of Nonlinear Panel Models with Multiple Unobserved Effects (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:1120
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