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Nonlinear Panel Models with Interactive Effects | | Mingli Chen
; Ivan Fernandez-Val
; Martin Weidner
; | | Date: |
17 Dec 2014 | | Abstract: | This paper considers estimation and inference on semiparametric nonlinear
panel single index models with predetermined explanatory variables and
interactive individual and time effects. These include static and dynamic
probit, logit, and Poisson models. Fixed effects conditional maximum likelihood
estimation is challenging because the log likelihood function is not concave in
the individual and time effects. We propose an iterative two-step procedure to
maximize the likelihood that is concave in each step. Under asymptotic
sequences where both the cross section and time series dimensions of the panel
pass to infinity at the same rate, we show that the fixed effects conditional
maximum likelihood estimator is consistent, but it has bias in the asymptotic
distribution due to the incidental parameter problem. We characterize the bias
and develop analytical and jackknife bias corrections that remove the bias from
the asymptotic distribution without increasing variance. In numerical examples,
we find that the corrections substantially reduce the bias and rmse of the
estimator in small samples, and produce confidence intervals with coverages
that are close to their nominal levels. | | Source: | arXiv, 1412.5647 | | Services: | Forum | Review | PDF | Favorites | |
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