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Optimally Combining Censored and Uncensored Datasets

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Author Info

  • Paul J. Devereux

    (UCLA)

  • Gautam Tripathi

    (University of Connecticut)

Abstract

Economists and other social scientists often face situations where they have access to two datasets that they can use but one set of data suffers from censoring or truncation. If the censored sample is much bigger than the uncensored sample, it is common for researchers to use the censored sample alone and attempt to deal with the problem of partial observation in some manner. Alternatively, they simply use only the uncensored sample and ignore the censored one so as to avoid biases. It is rarely the case that researchers use both datasets together, mainly because they lack guidance about how to combine them. In this paper, we develop a simple semiparametric framework for combining the censored and uncensored datasets so that the resulting estimators are consistent, asymptotically normal, and use all information optimally. No nonparametric smoothing is required to implement our estimators. To illustrate our results in an empirical setting, we show how to estimate the effect of changes in compulsory schooling laws on age at first marriage, a variable that is censored for younger individuals. We also demonstrate how refreshment samples for this application can be created by combining cohort information across census datasets. Results from a small simulation experiment suggest that the estimator proposed in this paper can work very well in finite samples.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2005-10.

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Length: 49 pages
Date of creation: Apr 2005
Date of revision: Oct 2007
Handle: RePEc:uct:uconnp:2005-10

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Web page: http://www.econ.uconn.edu/
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Related research

Keywords: Censoring; Empirical Likelihood; GMM; Refreshment samples; Truncation;

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References

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Cited by:
  1. Humlum, Maria Knoth & Kristoffersen, Jannie H. G. & Vejlin, Rune Majlund, 2014. "Timing of College Enrollment and Family Formation Decisions," IZA Discussion Papers 7905, Institute for the Study of Labor (IZA).
  2. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 901-37, December.
  3. Powdthavee, Nattavudh & Adireksombat, Kampon, 2010. "From Classroom to Wedding Aisle: The Effect of a Nationwide Change in the Compulsory Schooling Law on Age at First Marriage in the UK," IZA Discussion Papers 5019, Institute for the Study of Labor (IZA).

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