Optimally combining censored and uncensored datasets

• Published in: Journal of econometrics Vol. 151; no. 1; pp. 17 - 32
• Main Authors: Devereux, Paul J., Tripathi, Gautam
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2009; Elsevier; Elsevier Sequoia S.A
• Series: Journal of Econometrics
• Subjects: Applications; Censoring; Censoring Combining datasets Generalized method of moments; Combining datasets; Data aggregation; Data analysis; Econometrics; Estimating techniques; Exact sciences and technology; Generalized method of moments; Insurance, economics, finance; Mathematics; Nonparametric inference; Parameter estimation; Probability and statistics; Sciences and techniques of general use; Simulation; Statistics; Studies; Censoring; Generalized method of moments; Combining datasets; Censored data; Data analysis; Smoothing methods; Finite sample; Censored sample; Non parametric estimation; Statistical estimation; Semiparametric method; Asymptotic convergence; Economic data; Implementation; Statistical method; Simulation; Consistent estimator; Smoothing; Asymptotic normality; Econometrics
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2009.03.012

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. Results from a small simulation experiment suggest that the estimator proposed in this paper can work very well in finite samples.