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Pairwise Comparison Estimation of Censored Transformation Models

Author

Listed:
  • 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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    More about this item

    Keywords

    Transformation Models; Pairwise Comparison; Maximum Rank Correlation; Duration Analysis;
    All these keywords.

    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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