Estimation of an Endogenous Switching Regression Model with Discrete Dependent Variables: Monte-Carlo Analysis and Empirical Application of Three Estimators

Ayal Kimhi

No 232677, Working Papers from Hebrew University of Jerusalem, Center for Agricultural Economic Research

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.

Keywords: Agricultural; and; Food; Policy (search for similar items in EconPapers)
Pages: 41
Date: 1995
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/232677/files/h ... rkingpapers-9505.pdf (application/pdf)

Related works:
Journal Article: Estimation of an endogenous switching regression model with discrete dependent variables: Monte-Carlo analysis and empirical application of three estimators (1999)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:huaewp:232677

DOI: 10.22004/ag.econ.232677

Access Statistics for this paper

More papers in Working Papers from Hebrew University of Jerusalem, Center for Agricultural Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().