Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms

Kang, David (2020) Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms. Econometric Theory. ISSN 0266-4666

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Abstract

Nonparametric series regression often involves specification search over the tuning parameter, that is, evaluating estimates and confidence intervals with a different number of series terms. This paper develops pointwise and uniform inferences for conditional mean functions in nonparametric series estimations that are uniform in the number of series terms. As a result, this paper constructs confidence intervals and confidence bands with possibly data-dependent series terms that have valid asymptotic coverage probabilities. This paper also considers a partially linear model setup and develops inference methods for the parametric part uniform in the number of series terms. The finite sample performance of the proposed methods is investigated in various simulation setups as well as in an illustrative example, that is, the nonparametric estimation of the wage elasticity of the expected labor supply from Blomquist and Newey (2002, Econometrica 70, 2455-2480).

Item Type:
Journal Article
Journal or Publication Title:
Econometric Theory
Additional Information:
https://www.cambridge.org/core/journals/econometric-theory/article/inference-in-nonparametric-series-estimation-with-specification-searches-for-the-number-of-series-terms/8112698259D6E213EA068D28E9FE32AB The final, definitive version of this article has been published in the Journal, Econometric Theory, ? (?), pp ?-? 2020, © 2020 Cambridge University Press.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3301
Subjects:
ID Code:
143941
Deposited By:
Deposited On:
12 May 2020 12:40
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Sep 2020 03:01