"The Stochastic Frontier Model with Ordered Multiple Choices" (Job Market Paper)
(Revise and Resubmit at Journal of Productivity Analysis)
This paper develops the stochastic frontier model with multiple endogenous regimes to solve biased and inconsistent estimates due to sample selection bias. If we encompass heterogeneous observations into one regression equation, then the estimated parameters may be invalid. Moreover, if heterogeneous observations did not randomly fall into regression equations with reasonable coefficients, this statistically implies that sample selection bias exists. This paper extends the stochastic frontier model with two endogenous regimes to include multiple regimes and expand the empirical application. Based on the sample selection/regime-switching information, I derived the closed form of the likelihood function and the estimator of the technical efficiency index for the proposed model. Additionally, I rendered an estimate using the maximum likelihood estimation. In this empirical study, I analyzed the cost efficiency of doctoral-granting universities in the United States and applied this to the proposed model with three regimes. Evidence suggests that considering sample selection and the multiple-regime model is necessary when observations are heterogeneous.
"The Heckman Selection Model with Stochastic Frontier Analysis and Endogeneity"
This study developed the Heckman selection model with endogeneity and includes the stochastic frontier (SF) model as its main equation, which allowed us to solve the problems of selectivity and endogeneity bias simultaneously in the SF model, otherwise the estimated parameters are biased and inconsistent. The proposed model can be applied to three cases of endogeneity, including the endogeneity that exists only in the SF model (main equation), in the selection equation, or in both. For the proposed model, I derived the closed form of the likelihood function and the estimator of the technical efficiency index using information on selectivity and endogeneity. By using the two-step estimation, I was able to easily estimate the proposed model without having to undertake the complicated estimation procedure. In the empirical study, I analyzed the operating efficiency of air carriers in the United States. The results suggest that considering both selectivity and endogeneity is necessary, otherwise the predicted technical efficiency indices are invalid.
"The Double Hurdle Stochastic Frontier Model with Ordered Multiple Choices"
Developed the double hurdle model with the SF model and extends the two outcomes in the second selection stage to multiple possible outcomes.
Derived the closed form of the likelihood function and the estimator of the TE index based on the sample selection information for the proposed model.
"The Panel Stochastic Frontier Model with Endogenous Switching," with Hung-Pin Lai and Subal C. Kumbhakar
Extended the SF model with sample selection discussed in Lai (2015) to the panel version.
Conducted a Monte Carlo simulation to study the performance of the proposed estimator with finite sample.
"Overeducation: Effect on Wages and Deadweight Loss," with Yu-Hsia Chen
Estimated the effect of overeducation (undereducation) on wages by the maximum likelihood estimation to consider the sample selection bias problem caused by excluding the nonworkers’ sample. Also, considering the wage equation should be different between full-time workers and part-time workers.
Analyzed graphically the deadweight loss of overeducation of an individual and the entire society.