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An Exponential Endogenous Switching Regression with Correlated Random Coefficients

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  • Myoung-Jin Keay

    (Ness School of Management and Economics, South Dakota State University, Brookings, SD 57006, USA)

Abstract

This paper presents a method for estimating the average treatment effects (ATE) of an exponential endogenous switching model where the coefficients of covariates in the structural equation are random and correlated with the binary treatment variable. The estimating equations are derived under some mild identifying assumptions. We find that the ATE is identified, although each coefficient in the structural model may not be. Tests assessing the endogeneity of treatment and for model selection are provided. Monte Carlo simulations show that, in large samples, the proposed estimator has a smaller bias and a larger variance than the methods that do not take the random coefficients into account. This is applied to health insurance data of Oregon.

Suggested Citation

  • Myoung-Jin Keay, 2021. "An Exponential Endogenous Switching Regression with Correlated Random Coefficients," Econometrics, MDPI, vol. 10(1), pages 1-16, December.
    • Handle: RePEc:gam:jecnmx:v:10:y:2021:i:1:p:1-:d:707650
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