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Testing linearity against threshold effects: uniform inference in quantile regression

  • Antonio Galvao

    ()

  • Kengo Kato

    ()

  • Gabriel Montes-Rojas

    ()

  • Jose Olmo

    ()

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

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File URL: http://hdl.handle.net/10.1007/s10463-013-0418-9
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Article provided by Springer in its journal Annals of the Institute of Statistical Mathematics.

Volume (Year): 66 (2014)
Issue (Month): 2 (April)
Pages: 413-439

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Handle: RePEc:spr:aistmt:v:66:y:2014:i:2:p:413-439
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  1. He X. & Zhu L-X., 2003. "A Lack-of-Fit Test for Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1013-1022, January.
  2. Nobel, Andrew & Dembo, Amir, 1993. "A note on uniform laws of averages for dependent processes," Statistics & Probability Letters, Elsevier, vol. 17(3), pages 169-172, June.
  3. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  4. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  5. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March.
  6. Sokbae 'Simon' Lee & Myung Hwan Seo & Youngki Shin, 2010. "Testing for threshold effects in regression models," CeMMAP working papers CWP36/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, 03.
  8. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
  9. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
  10. Caner, Mehmet, 2002. "A Note On Least Absolute Deviation Estimation Of A Threshold Model," Econometric Theory, Cambridge University Press, vol. 18(03), pages 800-814, June.
  11. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
  12. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
  13. Yuzhi Cai, 2010. "Forecasting for quantile self-exciting threshold autoregressive time series models," Biometrika, Biometrika Trust, vol. 97(1), pages 199-208.
  14. Komunjer, Ivana & Vuong, Quang, 2010. "Semiparametric Efficiency Bound In Time-Series Models For Conditional Quantiles," Econometric Theory, Cambridge University Press, vol. 26(02), pages 383-405, April.
  15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  16. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
  17. Carrasco, Marine, 2002. "Misspecified Structural Change, Threshold, and Markov-switching models," Journal of Econometrics, Elsevier, vol. 109(2), pages 239-273, August.
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