Estimation of an endogenous switching regression model with discrete dependent variables: Monte-Carlo analysis and empirical application of three estimators

Ayal Kimhi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The performances of alternative two-stage estimators for the endogenous switching regression model with discrete dependent variables are compared, with regard to their usefulness as starting values for maximum likelihood estimation. This is especially important in the presence of large correlation coefficients, in which case maximum likelihood procedures have difficulties to converge. Monte-Carlo simulations indicate that an estimator that corrects for conditional heteroskedasticity of the residuals is superior in almost all instances, and especially when maximum likelihood is problematic. This result is also obtained in an empirical example in which off-farm work participation equations of farm women are conditional on farm work participation status.

Original languageAmerican English
Pages (from-to)225-241
Number of pages17
JournalEmpirical Economics
Volume24
Issue number2
DOIs
StatePublished - 1999

Keywords

  • Discrete dependent variables
  • Endogenous switching
  • Farm women's off-farm work participation
  • Monte-Carlo simulations
  • Two-stage estimators

Fingerprint

Dive into the research topics of 'Estimation of an endogenous switching regression model with discrete dependent variables: Monte-Carlo analysis and empirical application of three estimators'. Together they form a unique fingerprint.

Cite this