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Efficientt Conditional Quantile Estimation: The Time Series Case


  • Komunjer, Ivana
  • Vuong, Quang


In this paper we consider the problem of efficient estimation in conditional quantile models with time series data. Our first result is to derive the semiparametric efficiency bound in time series models of conditional quantiles; this is a nontrivial extension of a large body of work on efficient estimation, which has traditionally focused on models with independent and identically distributed data. In particular, we generalize the bound derived by New and Powell (1990) to the case where the data is weakly dependent and heterogeneous. We then proceed by constructing an M-estimator which achieves the semiparametric efficiency bound. Our efficient M-estimator is obtained by minimizing an objective function which depends on a nonparametric estimator of the conditional distribution of the variable of interest rather than its density.

Suggested Citation

  • Komunjer, Ivana & Vuong, Quang, 2006. "Efficientt Conditional Quantile Estimation: The Time Series Case," University of California at San Diego, Economics Working Paper Series qt78842570, Department of Economics, UC San Diego.
  • Handle: RePEc:cdl:ucsdec:qt78842570

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    Cited by:

    1. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    2. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    3. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    4. Marc Hallin & Catherine Vermandele & Bas J. M. Werker, 2008. "Semiparametrically efficient inference based on signs and ranks for median-restricted models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 389-412.
    5. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    6. Manganelli, Simone & White, Halbert & Kim, Tae-Hwan, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 957, European Central Bank.


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