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Length-bias Correction in Transformation Models with Supplementary Data

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  • Youngki Shin

Abstract

In this article, I propose an inferential procedure of monotone transformation models with random truncation points, which may not be observable. This class includes length-biased samples that are common in duration analysis. The proposed estimator can be applied to more general situations than existing estimators, since it imposes restrictions on neither the transformation function nor the error terms. Furthermore, it does not require observed truncation points either. It is sufficient for point identification to know the cdf of the truncation variable, which can be estimated from supplementary data that are easily found in applications. The estimator converges to a normal distribution at the rate of [image omitted] and Monte Carlo simulations confirm its robustness to error distributions in finite samples. For an empirical illustration, I estimate the effect of unemployment insurance benefits on unemployment duration, using length-biased microdata and supplementary macrodata.

Suggested Citation

  • Youngki Shin, 2009. "Length-bias Correction in Transformation Models with Supplementary Data," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 658-681.
    • Handle: RePEc:taf:emetrv:v:28:y:2009:i:6:p:658-681
    DOI: 10.1080/07474930903039246
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    References listed on IDEAS

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    1. Karen Needels & Walter Corson & Walter Nicholson, "undated". "Left Out of the Boom Economy: UI Recipients in the Late 1990s," Mathematica Policy Research Reports 09dfd8b030124a2799bf225dc, Mathematica Policy Research.
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    More about this item

    Keywords

    Duration models; Length-biased data; Rank estimation; Random truncation; Transformation model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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