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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Main Author: Myoung-Jin Keay
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Econometrics
Subjects:
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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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