A flexible parametric approach for estimating switching regime models and treatment effect parameters

Heng Chen, Yanqin Fan and Jisong Wu

Journal of Econometrics, 2014, vol. 181, issue 2, 77-91

Abstract: In this paper, we propose a flexible, parametric class of switching regime models allowing for both skewed and fat-tailed outcome and selection errors. Specifically, we model the joint distribution of each outcome error and the selection error via a newly constructed class of multivariate distributions which we call generalized normal mean–variance mixture distributions. We extend Heckman’s two-step estimation procedure for the Gaussian switching regime model to the new class of models. When the distributions of the outcome errors are asymmetric, we show that an additional correction term accounting for skewness in the outcome error distribution (besides the analogue of the well known inverse mill’s ratio) needs to be included in the second step regression. We use the two-step estimators of parameters in the model to construct simple estimators of average treatment effects and establish their asymptotic properties. Simulation results confirm the importance of accounting for skewness in the outcome errors in estimating both model parameters and the average treatment effect and the treatment effect for the treated.

Keywords: EM algorithm; Generalized normal mean–variance mixture; Inverse-mills ratio; Skewness; Two-step estimation (search for similar items in EconPapers)
JEL-codes: C31 C34 C51 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:181:y:2014:i:2:p:77-91

DOI: 10.1016/j.jeconom.2013.06.006

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