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Testing linearity against threshold effects: uniform inference in quantile regression

Author

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  • Antonio Galvao

    ()

  • Kengo Kato

    ()

  • Gabriel Montes-Rojas

    ()

  • Jose Olmo

    ()

Abstract

This paper develops a uniform test of linearity against threshold effects in the quantile regression framework. The test is based on the supremum of the Wald process over the space of quantile and threshold parameters. We establish the limiting null distribution of the test statistic for stationary weakly dependent processes, and propose a simulation method to approximate the critical values. The proposed simulation method makes the test easy to implement. Monte Carlo experiments show that the proposed test has good size and reasonable power against non-linear threshold models. Copyright The Institute of Statistical Mathematics, Tokyo 2014

Suggested Citation

  • Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
  • Handle: RePEc:spr:aistmt:v:66:y:2014:i:2:p:413-439
    DOI: 10.1007/s10463-013-0418-9
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    References listed on IDEAS

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    17. repec:hal:journl:peer-00732534 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    2. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    3. repec:bla:jtsera:v:38:y:2017:i:1:p:99-119 is not listed on IDEAS
    4. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    5. Sokbae (Simon) Lee & Hyunmin Park & Myung Hwan Seo & Youngki Shin, 2014. "A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent," CeMMAP working papers CWP39/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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