Distribution-free Estimation of Some Nonlinear Panel Data Models
نویسنده
چکیده
The notion of a conditional linear predictor is used as a distributionfree method for eliminating the individual-specific effects in a class of nonlinear, unobserved components panel data models. The methodology is applied to a general count model, which allows for individual dispersion in addition to an individual mean effect. As a corollary of the general results, the multinomial quasi-conditional maximum likelihood estimator is shown to be consistent and asymptotically normal when only the first two moments in the unobserved effects model have been correctly specified. This has important implications for analyzing count data in panel contexts. Simple, robust specification tests for this class of count models are also developed. A second example covers the case where the variance is proportional to the square of the mean, encompassing unobserved component gamma regression models for panel data. Models with serial correlation are briefly discussed.
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