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A note on self-weighted quantile estimation for infinite variance quantile autoregression models

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  • Yang, Xiao Rong
  • Zhang, Li Xin

Abstract

This article focuses attention on quantile autoregressive (QAR) models in which the autoregressive coefficients can be dependent on the quantile function. We use the self-weighted quantile regressive estimation for infinite variance QAR models. The asymptotic normality of the estimated parameters are established conditionally on lagged values of the response. In addition, the Wald test statistics are developed for the linear restriction on the parameters. Finally, we discuss the regression rank score test and empirical likelihood method as alternative inference approaches, which do not require the estimations of nuisance parameters.

Suggested Citation

  • Yang, Xiao Rong & Zhang, Li Xin, 2008. "A note on self-weighted quantile estimation for infinite variance quantile autoregression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2731-2738, November.
  • Handle: RePEc:eee:stapro:v:78:y:2008:i:16:p:2731-2738
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    References listed on IDEAS

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    1. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    4. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
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    Cited by:

    1. Marcel Carcea & Robert Serfling, 2015. "A Gini Autocovariance Function for Time Series Modelling," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 817-838, November.

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