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| Modello a Effetti Fissi Non Lineari× | Modello a Effetti Casuali Non Lineari× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1984 | 1981–2010 |
| Ideatore≠ | Gary Chamberlain | Heckman (1981); Chamberlain (1984); further systematized by Wooldridge (2010) |
| Tipo≠ | Panel data estimator | Panel data / nonlinear regression |
| Fonte seminale≠ | Chamberlain, G. (1984). Panel data. In Z. Griliches & M. D. Intriligator (Eds.), Handbook of Econometrics (Vol. 2, pp. 1247–1318). Elsevier. link ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Alias | nonlinear FE model, NLFE, conditional fixed effects model, incidental parameters model | nonlinear RE model, NLRE model, random effects nonlinear panel model, mixed nonlinear panel model |
| Correlati≠ | 5 | 1 |
| Sintesi≠ | The nonlinear fixed effects model extends fixed effects panel estimation to outcomes governed by nonlinear response functions — such as binary, count, or censored outcomes — while absorbing unobserved individual heterogeneity through unit-specific intercepts. Key special cases include conditional logit for binary outcomes and Poisson fixed effects for count data. | The nonlinear random effects model extends classical random effects estimation to settings where the outcome variable is binary, count-based, censored, or otherwise non-continuously distributed across panel units. It accounts for unobserved individual heterogeneity by treating unit-specific effects as random draws from a distribution, then integrating them out to form a likelihood that can be maximised over the structural parameters. |
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