Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246480 
Year of Publication: 
2019
Series/Report no.: 
Working Paper No. 2019-03
Publisher: 
Rutgers University, Department of Economics, New Brunswick, NJ
Abstract: 
Controlling the bias is central to estimating semiparametric models. Many methods have been developed to control bias in estimating conditional expectations while main- taining a desirable variance order. However, these methods typically do not perform well at moderate sample sizes. Moreover, and perhaps related to their performance, non-optimal windows are selected with undersmoothing needed to ensure the appro- priate bias order. In this paper, we propose a recursive differencing estimator for conditional expectations. When this method is combined with a bias control targeting the derivative of the semiparametric expectation, we are able to obtain asymptotic normality under optimal windows. As suggested by the structure of the recursion, in a wide variety of triple index designs, the proposed bias control performs much better at moderate sample sizes than regular or higher order kernels and local polynomials.
Subjects: 
semiparametric model
bias reduction
conditional expectation
JEL: 
C1
C14
Document Type: 
Working Paper

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