Salīdzināt metodes
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| Nelineārā paneļu datu analīze× | Puasona un negatīvās binomiālās regresijas× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986–2010 | 1998 |
| Autors≠ | Cheng Hsiao; Jeffrey M. Wooldridge | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tips≠ | Panel data model (nonlinear) | Generalized linear model for count data |
| Pirmavots≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Citi nosaukumi | nonlinear panel models, panel nonlinear econometrics, fixed-effects nonlinear models, random-effects nonlinear models | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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