Ulric B. and Evelyn L. Bray Social Sciences Seminar

Wednesday, May 18, 2022
4:00pm to 5:00pm
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Online Event
Recursive Differencing for Estimating Semiparametric Models
Roger Klein, Professor of Economics, Rutgers University,
Chan Shen, Associate Professor, Penn State,

Abstract: Controlling the bias is central to estimating semiparametric models. Many methods have been developed to control bias in estimating conditional expectations while maintaining 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 appropriate 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.

Written with Chan Shen

For more information, please contact Letty Diaz by phone at 626-395-1255 or by email at letty.diaz@caltech.edu.