Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables |
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Authors: | Jeffrey M Wooldridge |
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Institution: | Department of Economics, Michigan State University, 486 W. Circle Drive, 110 Marshall-Adams Hall, East Lansing, MI 48824-1038, United States |
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Abstract: | I propose a quasi-maximum likelihood framework for estimating nonlinear models with continuous or discrete endogenous explanatory variables. Joint and two-step estimation procedures are considered. The joint procedure is a quasi-limited information maximum likelihood procedure, as one or both of the log likelihoods may be misspecified. The two-step control function approach is computationally simple and leads to straightforward tests of endogeneity. In the case of discrete endogenous explanatory variables, I argue that the control function approach can be applied with generalized residuals to obtain average partial effects. I show how the results apply to nonlinear models for fractional and nonnegative responses. |
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Keywords: | C13 C21 C25 |
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