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Efficientt Conditional Quantile Estimation: The Time Series Case

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  • Komunjer, Ivana
  • Vuong, Quang

Abstract

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.

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Bibliographic Info

Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt78842570.

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Date of creation: 01 Oct 2006
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Handle: RePEc:cdl:ucsdec:qt78842570

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Keywords: semiparametric efficientcy; time series models; dependence; parametric submodels; conditional quantiles;

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Cited by:
  1. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
  2. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
  3. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.

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