Abstract
The so-called “fixed effects” approach to the estimation of panel data models suffers from the limitation that it is not possible to estimate the coefficients on explanatory variables that are time-invariant. This is in contrast to a “random effects” approach, which achieves this by making much stronger assumptions on the relationship between the explanatory variables and the individual-specific effect. In a linear model, it is possible to obtain the best of both worlds by making random effects-type assumptions on the time-invariant explanatory variables while maintaining the flexibility of a fixed effects approach when it comes to the time-varying covariates. This article attempts to do the same for some popular nonlinear models.
Original language | English (US) |
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Pages (from-to) | 543-558 |
Number of pages | 16 |
Journal | Journal of Business and Economic Statistics |
Volume | 35 |
Issue number | 4 |
DOIs | |
State | Published - Oct 2 2017 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Social Sciences (miscellaneous)
- Economics and Econometrics
- Statistics, Probability and Uncertainty
Keywords
- Fixed effects
- Nonlinear models
- Panel data