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Asymptotic behavior of regression quantiles in non-stationary, dependent cases

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  • Portnoy, Stephen
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    Abstract

    Regression quantiles provide a natural and powerful approach for robust analysis of the general linear model. However, departures from independence and stationarity of the errors can have an extremely potent effect on statistical analysis. Here, a Bahadur representation for regression quantiles is provided for error processes which are highly non-stationary (i.e., for which there is a nonvanishing bias term) and which are close to being m-dependent. The conditions for dependence are based on a decomposition of Chanda, Puri, and Ruymgaart which covers linear processes; and, hence, includes ARMA processes.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 38 (1991)
    Issue (Month): 1 (July)
    Pages: 100-113

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    Handle: RePEc:eee:jmvana:v:38:y:1991:i:1:p:100-113

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    Keywords: linear models regression quantiles non-stationary processes dependent errors;

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    Cited by:
    1. Komunjer, Ivana, 2002. "Quasi-Maximum Likelihood Estimation for Conditional Quantiles," Working Papers 1139, California Institute of Technology, Division of the Humanities and Social Sciences.
    2. Tae-Hwan Kim, & Christophe Muller, 2012. "Bias Transmission and Variance Reduction in Two-Stage Quantile Regression," AMSE Working Papers 1221, Aix-Marseille School of Economics, Marseille, France.
    3. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    4. Mukherjee, Kanchan, 2000. "Linearization Of Randomly Weighted Empiricals Under Long Range Dependence With Applications To Nonlinear Regression Quantiles," Econometric Theory, Cambridge University Press, vol. 16(03), pages 301-323, June.
    5. Joan Jasiak & C. Gourieroux, 2006. "Dynamic Quantile Models," Working Papers 2006_4, York University, Department of Economics.
    6. Fitzenberger, Bernd, 1998. "The moving blocks bootstrap and robust inference for linear least squares and quantile regressions," Journal of Econometrics, Elsevier, vol. 82(2), pages 235-287, February.
    7. Fitzenberger, Bernd, 1994. "A note on estimating censored quantile regressions," Discussion Papers 14, University of Konstanz, Center for International Labor Economics (CILE).
    8. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.
    9. Neocleous, Tereza & Portnoy, Stephen, 2008. "On monotonicity of regression quantile functions," Statistics & Probability Letters, Elsevier, vol. 78(10), pages 1226-1229, August.
    10. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves without Crossing," Sciences Po publications info:hdl:2441/5rkqqmvrn4t, Sciences Po.
    11. Gounder, Rukmani & Xing, Zhongwei, 2012. "Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji," Economic Modelling, Elsevier, vol. 29(3), pages 787-794.
    12. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    13. George Kouretas & Leonidas Zarangas, 2005. "Conditional autoregressive valu at risk by regression quantile: Estimatingmarket risk for major stock markets," Working Papers 0521, University of Crete, Department of Economics.

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