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Bias Transmission and Variance Reduction in Two-Stage Quantile Regression

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  • Tae-Hwan Kim

    ()
    (School of Economics, Yonsei University - Yonsei University)

  • Christophe Muller

    ()
    (AMSE - Aix-Marseille School of Economics - Aix-Marseille Univ. - Centre national de la recherche scientifique (CNRS) - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Ecole Centrale Marseille (ECM))

Abstract

In this paper, we propose a variance reduction method for quantile regressions with endogeneity problems. First, we derive the asymptotic distribution of two-stage quantile estimators based on the fitted-value approach under very general conditions on both error terms and exogenous variables. Second, we exhibit a bias transmission property derived from the asymptotic representation of our estimator. Third, using a reformulation of the dependent variable, we improve the efficiency of the two-stage quantile estimators by exploiting a trade-off between an asymptotic bias confined to the intercept estimator and a reduction of the variance of the slope estimator. Monte Carlo simulation results show the excellent performance of our approach. In particular, by combining quantile regressions with first-stage trimmed least-squares estimators, we obtain more accurate slope estimates than 2SLS, 2SLAD and other estimators for a broad range of distributions.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00793372.

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Date of creation: Jun 2012
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Handle: RePEc:hal:wpaper:halshs-00793372

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Related research

Keywords: Two-Stage Estimation; Variance Reduction; Quantile Regression; Asymptotic Bias;

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  1. Honore, Bo E & Hu, Luojia, 2004. "On the Performance of Some Robust Instrumental Variables Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 30-39, January.
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  3. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
  4. Kim, Tae-Hwan & White, Halbert, 2000. "James-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator," University of California at San Diego, Economics Working Paper Series qt4zq9k3qh, Department of Economics, UC San Diego.
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  6. Powell, James L, 1983. "The Asymptotic Normality of Two-Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 51(5), pages 1569-75, September.
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  8. Jaume Garcia & Pedro J. Hernández & Ángel López Nicolás, 1998. "How wide is the gap? An investigation of gender wage differences using quantile regression," Economics Working Papers 287, Department of Economics and Business, Universitat Pompeu Fabra.
  9. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
  10. Portnoy, Stephen, 1991. "Asymptotic behavior of regression quantiles in non-stationary, dependent cases," Journal of Multivariate Analysis, Elsevier, vol. 38(1), pages 100-113, July.
  11. Chernozhukov, Victor & Imbens, Guido W. & Newey, Whitney K., 2007. "Instrumental variable estimation of nonseparable models," Journal of Econometrics, Elsevier, vol. 139(1), pages 4-14, July.
  12. Tae-Hwan Kim & Christophe Muller, 2004. "Two-stage quantile regression when the first stage is based on quantile regression," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 218-231, 06.
  13. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
  14. Alberto Abadie & Joshua Angrist & Guido Imbens, 1999. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Working papers 99-16, Massachusetts Institute of Technology (MIT), Department of Economics.
  15. Sakata, Shinichi, 2007. "Instrumental variable estimation based on conditional median restriction," Journal of Econometrics, Elsevier, vol. 141(2), pages 350-382, December.
  16. Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(05), pages 793-813, December.
  17. Thanaset Chevapatrakul & Tae-Hwan Kim & Paul Mizen, 2009. "The Taylor Principle and Monetary Policy Approaching a Zero Bound on Nominal Rates: Quantile Regression Results for the United States and Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(8), pages 1705-1723, December.
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  19. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 0957, European Central Bank.
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Cited by:
  1. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," Working Papers halshs-00854527, HAL.
  2. William Miles & Sam Schreyer, 2012. "Is monetary policy non-linear in Indonesia, Korea, Malaysia, and Thailand? A quantile regression analysis," Asian-Pacific Economic Literature, Asia Pacific School of Economics and Government, The Australian National University, vol. 26(2), pages 155-166, November.

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