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

Posted: 24 May 1999

See all articles by Ayal Kimhi

Ayal Kimhi

Hebrew University of Jerusalem - Department of Environmental Economics & Management; Faculty of Agriculture, Food and Environment

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.

JEL Classification: C35, J22, Q12

Suggested Citation

Kimhi, Ayal, Estimation of an Endogenous Switching Regression Model with Discrete Dependent Variables: Monte-Carlo Analysis and Empirical Application of Three Estimators. Available at SSRN: https://ssrn.com/abstract=164832

Ayal Kimhi (Contact Author)

Hebrew University of Jerusalem - Department of Environmental Economics & Management ( email )

Rehovot, 76100
Israel

HOME PAGE: http://departments.agri.huji.ac.il/economics/kimhi.html

Faculty of Agriculture, Food and Environment ( email )

Jerusalem
Israel

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