Consistent specification tests for semiparametric/nonparametric models based on series estimation methods Academic Article

abstract

• This paper considers the problem of consistent model specification tests using series estimation methods. The null models we consider in this paper all contain some nonparametric components. A leading case we consider is to test for an additive partially linear model. The null distribution of the test statistic is derived using a central limit theorem for Hilbert-valued random arrays. The test statistic is shown to be able to detect local alternatives that approach the null models at the order of Op(n-1/2). We show that the wild bootstrap method can be used to approximate the null distribution of the test statistic. A small Monte Carlo simulation is reported to examine the finite sample performance of the proposed test. We also show that the proposed test can be easily modified to obtain series-based consistent tests for other semiparametric/nonparametric models. 2002 Elsevier Science B.V. All rights reserved.

authors

• Li, Qi

published proceedings

• JOURNAL OF ECONOMETRICS

author list (cited authors)

• Li, Q., Hsiao, C., & Zinn, J.

citation count

• 49

complete list of authors

• Li, Q||Hsiao, C||Zinn, J

publication date

• February 2003

publisher

• Elsevier  Publisher

published in

• Journal of Econometrics  Journal

keywords

• Consistent Tests
• Semiparametric Models
• Series Estimation
• Wild Bootstrap

Digital Object Identifier (DOI)

• 10.1016/S0304-4076(02)00198-7

start page

• 295

end page

• 325

volume

• 112

issue

• 2

URL

• http://dx.doi.org/10.1016/s0304-4076(02)00198-7