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Pairwise Comparison Estimation of Censored Transformation Models

Author

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  • Shakeeb Khan

    () (University of Rochester)

  • Elie Tamer

    (Princeton)

Abstract

In this paper a pairwise comparison estimation procedure is proposed for the regression coefficients in a censored transformation model. The main advantage of the new estimator is that it can accommodate covariate dependent censoring without the requirement of smoothing parameters, trimming procedures, or stringent tail behavior restrictions. We also modify the pairwise estimator for other variations of the transformation model and propose estimators for the transformation function itself, as well as regression coefficients in heteroskedastic and panel data models. The estimators are shown to converge at the parametric (root-$n$) rate, and the results of a small scale simulation study indicate they perform well in finite samples. We illustrate our estimator using the Stanford Heart Transplant data and marriage length data from the CPS fertility supplement.

Suggested Citation

  • Shakeeb Khan & Elie Tamer, 2002. "Pairwise Comparison Estimation of Censored Transformation Models," RCER Working Papers 495, University of Rochester - Center for Economic Research (RCER).
  • Handle: RePEc:roc:rocher:495
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    File URL: http://rcer.econ.rochester.edu/RCERPAPERS/rcer_495.pdf
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    References listed on IDEAS

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    1. repec:cup:etheor:v:10:y:1994:i:2:p:372-95 is not listed on IDEAS
    2. Gorgens, Tue & Horowitz, Joel L., 1999. "Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, pages 155-191.
    3. Chaudhuri, Probal, 1991. "Global nonparametric estimation of conditional quantile functions and their derivatives," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 246-269, November.
    4. Geert Ridder, 1990. "The Non-Parametric Identification of Generalized Accelerated Failure-Time Models," Review of Economic Studies, Oxford University Press, vol. 57(2), pages 167-181.
    5. Abrevaya, Jason, 1999. "Computation of the maximum rank correlation estimator," Economics Letters, Elsevier, vol. 62(3), pages 279-285, March.
    6. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    7. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
    8. Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
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    13. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-137, January.
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    More about this item

    Keywords

    Transformation Models; Pairwise Comparison; Maximum Rank Correlation; Duration Analysis;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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