Non-linear Panel Data Models
Panel or longitudinal data are becoming increasingly popular in applied work as they offer a number of advantages over pure cross-sectional or pure time-series data. They allow researchers to model unobserved heterogeneity at the level of the observational unit, where the latter may be an individual, a household, a firm or a country. This article describes several estimation methods that are available for nonlinear panel data models, that is, models which are nonlinear in the parameters of interest and which include models that arise frequently in applied work, such as discrete choice models and limited dependent variable models, among others.
KeywordsConditional maximum likelihood Conditioning variables Fixed effects;heteroskedasticity Incidental parameters Individual effects Logit models Longitudinal data Nonlinear panel data models Quadrature procedures Random effects Random variables Tobit models
JEL ClassificationsC23 C33
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