Abstract
This article examines the null limit distribution of the quasi-likelihood ratio (QLR) statistic for testing linearity condition against the smooth transition autoregressive (STAR) model. We explicitly show that the QLR test statistic weakly converges to a functional of a multivariate Gaussian process under the null of linearity, which is done by resolving the issue of identification problem arises in two different ways under the null. In contrast with the Lagrange multiplier test that is widely employed for testing the linearity condition, the proposed QLR statistic has an omnibus power, and thus, it complements the existing testing procedure. We show the empirical relevance of our test by testing the neglected nonlinearity of the US fiscal multipliers and growth rates of US unemployment. These empirical examples demonstrate that the QLR test is useful for detecting the nonlinear structure among economic variables.
| Original language | English |
|---|---|
| Pages (from-to) | 966-984 |
| Number of pages | 19 |
| Journal | Econometric Reviews |
| Volume | 41 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Funding Information:The editor, Esfandiar Maasoumi, associate editor, and two anonymous referees provided very helpful comments for which we are most grateful. Part of the work for this article was done when the second and third authors were visiting the Department of Economics, The Chinese University of Hong Kong and the School of Economics and Finance, Queensland University of Technology, respectively, whose kind hospitalities are gratefully acknowledged. The authors have benefitted from discussions with Otilia Boldea, Tae-Hwan Kim and Byungsam Yoo. Cho also acknowledges financial support from the Yonsei University Research Grant of 2021. Responsibility for any errors and shortcomings in this work remains ours.
Funding Information:
The editor, Esfandiar Maasoumi, associate editor, and two anonymous referees provided very helpful comments for which we are most grateful. Part of the work for this article was done when the second and third authors were visiting the Department of Economics, The Chinese University of Hong Kong and the School of Economics and Finance, Queensland University of Technology, respectively, whose kind hospitalities are gratefully acknowledged. The authors have benefitted from discussions with Otilia Boldea, Tae-Hwan Kim and Byungsam Yoo. Cho also acknowledges financial support from the Yonsei University Research Grant of 2021. Responsibility for any errors and shortcomings in this work remains ours.
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Economics and Econometrics
