Estimation of Some Nonlinear Panel Data Models With Both Time-Varying and Time-Invariant Explanatory Variables

Bo E. Honoré, Michaela Kesina

Research output: Contribution to journalArticle

4 Scopus citations

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 languageEnglish (US)
Pages (from-to)543-558
Number of pages16
JournalJournal of Business and Economic Statistics
Volume35
Issue number4
DOIs
StatePublished - 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

Fingerprint Dive into the research topics of 'Estimation of Some Nonlinear Panel Data Models With Both Time-Varying and Time-Invariant Explanatory Variables'. Together they form a unique fingerprint.

  • Cite this