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Optimally Combining Censored and Uncensored Datasets

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  • Devereux, Paul J.
  • Tripathi, Gautam

Abstract

We develop a simple semiparametric framework for combining censored and uncensored samples 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 find positive effects of the laws on age at first marriage but the effects are much smaller than would be inferred if one ignored the censoring problem. 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 C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 6990.

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Date of creation: Oct 2008
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Handle: RePEc:cpr:ceprdp:6990

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Keywords: age at first marriage; censored data; compulsory schooling;

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Cited by:
  1. Maria K. Humlum & Jannie H.G. Kristoffersen & Rune Vejlin, 2012. "Timing of College Enrollment and Family Formation Decisions," Economics Working Papers 2012-01, School of Economics and Management, University of Aarhus.
  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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