Sequentially testing polynomial model hypotheses using power transforms of regressors

Jin Seo Cho, Peter C.B. Phillips

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13 Citations (Scopus)


We provide a methodology for testing a polynomial model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips (Journal of Econometrics, 2015, 187, 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi-likelihood ratio test and deal with the new multifold identification problem that arises under the null of the polynomial model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown polynomial degree in a model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in both identifying and estimating unknown polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (Journal of Political Economy, 1958, 66, 281–302; Schooling, Experience and Earnings, Columbia University Press, 1974) equation using data from Card (in Christofides, Grant, and Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp, University of Toronto Press, 1995, 201-222) and Bierens and Ginther (Empirical Economics, 2001, 26, 307–324). The results confirm that the standard Mincer log earnings equation is readily shown to be misspecified. The applications consider different datasets and examine the impact of nonlinear effects of experience and schooling on earnings, allowing for flexibility in the respective polynomial representations.

Original languageEnglish
Pages (from-to)141-159
Number of pages19
JournalJournal of Applied Econometrics
Issue number1
Publication statusPublished - 2018 Jan 1

Bibliographical note

Funding Information:
The Co-editor, Jonathan Wright, and two anonymous referees provided helpful comments and suggestions, for which we are most grateful. We also have benefited from discussions with Seungmoon Choi, Chirok Han, Tae-Hwan Kim, Dong Jin Lee, Jong Hwa Lee, Xun Lu, Hyungsik Roger Moon, Tatsushi Oka, Myung Hwan Seo, Zhentao Shi, Xiaohu Wang, Zhenlin Yang, and conference participants at SETA and NZESG (University of Waikato, Waikato 2016) and CUHK econometrics workshop (2017). Cho acknowledges research support from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2010-332-B00025), and Phillips acknowledges support from the NSF under Grant No. SES 12-58258 and the Kelly Foundation at the University of Auckland.

Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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