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

Byunghoon Kang ()

No 240829404, Working Papers from Lancaster University Management School, Economics Department

Abstract: Nonparametric series estimation often involves specification search over the different number of series terms due to the unknown smoothness of underlying function. This paper considers pointwise inference in the nonparametric series regression for the conditional mean and introduces test based on the supremum of t-statistics over different series terms. I show that proposed test has correct asymptotic size and it can be used to construct confidence intervals that have correct asymptotic coverage probability uniform in the number of series terms. With possibly large bias in this setup, I also consider infimum of the t-statistics which is shown to reduce size distortions in such case. Asymptotic distribution of the test statistics, asymptotic size, and local power results are derived. I investigate the performance of the proposed tests and CIs in various simulation setups as well as an illustrative example, nonparametric estimation of wage elasticity of the expected labor supply from Blomquist and Newey (2002). I also extend our inference methods to the partially linear model setup.

Keywords: Nonparametric series regression; Pointwise confidence interval; Smoothing parameter choice; Specification search; Undersmoothing (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.lancaster.ac.uk/media/lancaster-univers ... casterWP2018_010.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:lan:wpaper:240829404

Access Statistics for this paper

More papers in Working Papers from Lancaster University Management School, Economics Department Contact information at EDIRC.
Bibliographic data for series maintained by Giorgio Motta ().