Arbeitspapier
Recursive Differencing for Estimating Semiparametric Models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 2019-03
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
- Thema
-
semiparametric model
bias reduction
conditional expectation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Shen, Chan
Klein, Roger W.
- Ereignis
-
Veröffentlichung
- (wer)
-
Rutgers University, Department of Economics
- (wo)
-
New Brunswick, NJ
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Shen, Chan
- Klein, Roger W.
- Rutgers University, Department of Economics
Entstanden
- 2019
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