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An algorithm for censored quantile regressions

Author

Listed:
  • Thanasis Stengos

    (University of Guelph)

  • Dianqin Wang

    (University of Guelph)

Abstract

In this paper, we present an algorithm for Censored Quantile Regression (CQR) estimation problems. Our method permits CQR estimation problems to be solved more efficiently and reliably than was hitherto possible. It guarantees to find a high quality estimator in O(k×n²) operations with k regressors and n observations, which is much less than the existing algorithms for CQR problems.

Suggested Citation

  • Thanasis Stengos & Dianqin Wang, 2007. "An algorithm for censored quantile regressions," Economics Bulletin, AccessEcon, vol. 3(1), pages 1-9.
    • Handle: RePEc:ebl:ecbull:eb-06c20071
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    References listed on IDEAS

    as
    1. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    2. 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.
    3. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    4. Pinkse, C. A. P., 1993. "On the computation of semiparametric estimates in limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 185-205, July.
    5. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Cencored Quantile Regression;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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