Recursive Differencing for Estimating Semiparametric Models

Chan Shen ()
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Chan Shen: Pennsylvania State University

Departmental Working Papers from Rutgers University, Department of Economics

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.

Keywords: semiparametric model; bias reduction; conditional expectation (search for similar items in EconPapers)
JEL-codes: C1 C14 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2019-11-24
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201903

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