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An Exponential Endogenous Switching Regression with Correlated Random Coefficients
| Content Provider | MDPI |
|---|---|
| Author | Keay, Myoung-Jin |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 1 |
| e-ISSN | 22251146 |
| DOI | 10.3390/econometrics10010001 |
| Journal | Econometrics |
| Issue Number | 1 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-21 |
| Access Restriction | Open |
| Subject Keyword | Econometrics Mathematical Social Sciences Correlated Random Coefficient Average Treatment Effect Exponential Model Endogenous Switching Regression |
| Content Type | Text |
| Resource Type | Article |
