Handling Endogenous Regressors by Joint Estimation Using Copulas
Arizona State University (ASU) - W.P. Carey School of Business
Cornell University - Samuel Curtis Johnson Graduate School of Management
January 17, 2012
Marketing Science, Forthcoming
Johnson School Research Paper Series No. 19-2012
We propose a new statistical instrument-free method to tackle the endogeneity problem. The proposed method models the joint distribution of the endogenous regressor and the error term in the structural equation of interest (the structural error) using a copula method, and makes inferences on the model parameters by maximizing the likelihood derived from the joint distribution. Similar to the “exclusion restriction” in instrumental variable methods, extant instrument-free methods require the assumption that the unobserved instruments are exogenous, a requirement that is difficult to meet. The proposed method does not require such an assumption. Other benefits of the proposed method are that it allows modeling of discrete endogenous regressors and offers a new solution to the slope endogeneity problem. In addition to linear models, the method is applicable to the popular random coefficient logit model with both aggregate data and individual data. We demonstrate the performance of the proposed method via a series of simulation studies and an empirical example.
Number of Pages in PDF File: 51
Keywords: Endogeneity, Copula Method, Instrumental Variables, Two-Stage Least Squares, Linear Regression Model, Logit Model, Random CoefficientAccepted Paper Series
Date posted: March 20, 2012 ; Last revised: June 27, 2012
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo4 in 0.718 seconds