An Exponential Endogenous Switching Regression with Correlated Random Coefficients
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 m...
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MDPI AG
2021-12-01
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Online Access: | https://www.mdpi.com/2225-1146/10/1/1 |
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author | Myoung-Jin Keay |
author_facet | Myoung-Jin Keay |
author_sort | Myoung-Jin Keay |
collection | DOAJ |
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. |
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institution | Directory Open Access Journal |
issn | 2225-1146 |
language | English |
last_indexed | 2024-04-11T19:59:42Z |
publishDate | 2021-12-01 |
publisher | MDPI AG |
record_format | Article |
series | Econometrics |
spelling | doaj.art-f100483134a3471d82e17025f6b0564a2022-12-22T04:05:45ZengMDPI AGEconometrics2225-11462021-12-01101110.3390/econometrics10010001An Exponential Endogenous Switching Regression with Correlated Random CoefficientsMyoung-Jin Keay0Ness School of Management and Economics, South Dakota State University, Brookings, SD 57006, USAThis 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.https://www.mdpi.com/2225-1146/10/1/1Correlated Random Coefficientaverage treatment effectexponential modelendogenous switching regression |
spellingShingle | Myoung-Jin Keay An Exponential Endogenous Switching Regression with Correlated Random Coefficients Econometrics Correlated Random Coefficient average treatment effect exponential model endogenous switching regression |
title | An Exponential Endogenous Switching Regression with Correlated Random Coefficients |
title_full | An Exponential Endogenous Switching Regression with Correlated Random Coefficients |
title_fullStr | An Exponential Endogenous Switching Regression with Correlated Random Coefficients |
title_full_unstemmed | An Exponential Endogenous Switching Regression with Correlated Random Coefficients |
title_short | An Exponential Endogenous Switching Regression with Correlated Random Coefficients |
title_sort | exponential endogenous switching regression with correlated random coefficients |
topic | Correlated Random Coefficient average treatment effect exponential model endogenous switching regression |
url | https://www.mdpi.com/2225-1146/10/1/1 |
work_keys_str_mv | AT myoungjinkeay anexponentialendogenousswitchingregressionwithcorrelatedrandomcoefficients AT myoungjinkeay exponentialendogenousswitchingregressionwithcorrelatedrandomcoefficients |