ENTROPY-BASED ESTIMATION AND INFERENCE IN BINARY RESPONSE MODELS UNDER ENDOGENEITY

This paper considers estimation and inference for the binary response model in the case where endogenous variables are included as arguments of the unknown link function. Semiparametric estimators are proposed that avoid the parametric assumptions underlying the likelihood approach as well as the loss of precision when using nonparametric estimation. Suggestions are made for how the utility maximization decision model can be altered to permit attributes to vary across alternatives.


Issue Date:
2004
Publication Type:
Conference Paper/ Presentation
Record Identifier:
http://ageconsearch.umn.edu/record/20319
PURL Identifier:
http://purl.umn.edu/20319
Total Pages:
22
Series Statement:
Selected Paper




 Record created 2017-04-01, last modified 2018-01-22

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