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Semiparametrically efficient inference based on signs and ranks for median-restricted models

  • Marc Hallin
  • Catherine Vermandele
  • Bas J. M. Werker

Since the pioneering work of Koenker and Bassett, median-restricted models have attracted considerable interest. Attention in these models, so far, has focused on least absolute deviation (auto-)regression quantile estimation and the corresponding sign tests. These methods use a pseudolikelihood that is based on a double-exponential reference density and enjoy quite attractive properties of root "n" consistency (for estimators) and distribution freeness (for tests). The paper extends these results to general, i.e. not necessarily double-exponential, reference densities. Using residual signs and ranks (not "signed ranks") and a general reference density "f", we construct estimators that remain root "n" consistent, irrespective of the true underlying density "g" (i.e. also for "g"  /="f"). However, instead of reaching semiparametric efficiency bounds under double-exponential "g", they reach these bounds when "g" coincides with the chosen reference density "f". Moreover, we show that choosing reference densities other than the double-exponential in applications can lead to sizable gains in efficiency. The particular case of median regression is treated in detail; extensions to general quantile regression, heteroscedastic errors and time series models are briefly described. The performance of the method is also assessed by simulation and illustrated on financial data. Copyright (c) 2008 The Authors.

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Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Volume (Year): 70 (2008)
Issue (Month): 2 ()
Pages: 389-412

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Handle: RePEc:bla:jorssb:v:70:y:2008:i:2:p:389-412
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  1. Jeganathan, P., 1995. "Some Aspects of Asymptotic Theory with Applications to Time Series Models," Econometric Theory, Cambridge University Press, vol. 11(05), pages 818-887, October.
  2. Swensen, Anders Rygh, 1985. "The asymptotic distribution of the likelihood ratio for autoregressive time series with a regression trend," Journal of Multivariate Analysis, Elsevier, vol. 16(1), pages 54-70, February.
  3. Hallin, M. & Werker, B.J.M., 2003. "Semiparametric efficiency, distribution-freeness and invariance," Other publications TiSEM fe20db00-786a-4261-9999-6, Tilburg University, School of Economics and Management.
  4. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-37, January.
  5. Marc Hallin & Madan Lal Puri, 1994. "Aligned rank tests for linear models with autocorrelated errors," ULB Institutional Repository 2013/2045, ULB -- Universite Libre de Bruxelles.
  6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  7. Drost, F.C. & Klaassen, C.A.J. & Werker, B.J.M., 1997. "Adaptive estimation in time-series models," Other publications TiSEM aa253902-af93-4e1e-b974-2, Tilburg University, School of Economics and Management.
  8. Newey, Whitney K, 1990. "Semiparametric Efficiency Bounds," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 99-135, April-Jun.
  9. Zhou, Yong & Liang, Hua, 2000. "Asymptotic Normality for L1 Norm Kernel Estimator of Conditional Median under [alpha]-Mixing Dependence," Journal of Multivariate Analysis, Elsevier, vol. 73(1), pages 136-154, April.
  10. Hallin, M. & Puri, L.M., 1992. "Aligned Rank tests for Linear Models with Autocorrelated Error Terms," Papers 9202, Universite Libre de Bruxelles - C.E.M.E..
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