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Testing a parametric transformation model versus a nonparametric alternative

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journal contribution
posted on 29.07.2020, 13:19 by Arkadiusz M Szydlowski
Despite an abundance of semiparametric estimators of the transformation model, no procedure has been proposed yet to test the hypothesis that the transformation function belongs to a finite dimensional parametric family against a nonparametric alternative. In this article, we introduce a bootstrap test based on integrated squared distance between a nonparametric estimator and a parametric null. As a special case, our procedure can be used to test the parametric specification of the integrated baseline hazard in a semiparametric mixed proportional hazard model. We investigate the finite sample performance of our test in a Monte Carlo study. Finally, we apply the proposed test to Kennan’s strike durations data.

History

Citation

Econometric Theory, 00, 2020, 1–36. doi:10.1017/S0266466619000355

Author affiliation

School of Business

Version

AM (Accepted Manuscript)

Published in

Econometric Theory

Volume

00

Pagination

1-36

Publisher

Cambridge University Press (CUP)

issn

0266-4666

Acceptance date

24/10/2019

Copyright date

2020

Available date

12/05/2020

Language

en

Publisher version

https://www.cambridge.org/core/journals/econometric-theory/article/testing-a-parametric-transformation-model-versus-a-nonparametric-alternative/F63C6B7C621FB52D5461A3674F9A1EB5#fndtn-information