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TESTING A PARAMETRIC TRANSFORMATION MODEL VERSUS A NONPARAMETRIC ALTERNATIVE

Published online by Cambridge University Press:  12 May 2020

Arkadiusz Szydłowski
Arkadiusz Szydłowski*
Affiliation:
University of Leicester
*
Address correspondence to Arkadiusz Szydłowski, Division of Economics, University of Leicester, University Road, Leicester LE1 7RH, UK; email: ams102@le.ac.uk
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Abstract

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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.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 36 , Issue 5 , October 2020 , pp. 871 - 906
Copyright
© Cambridge University Press 2020

Footnotes

I am grateful to Joel Horowitz and Elie Tamer for their encouragement and valuable suggestions. I would also like to thank the co-editor, three anonymous referees, Yu Zhu, and participants at WIEM 2015 and ESEM 2016 conferences for their comments. This research used the ALICE High Performance Computing Facility at the University of Leicester and the Social Sciences Computing Cluster at Northwestern University.

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