A small-sample estimator for the sample-selection model
AbstractA semiparametric estimator for evaluating the parameters of data generated under a sample selection process is developed. This estimator is based on the generalized maximum entropy estimator and performs well for small and ill-posed samples. Theoretical and sampling comparisons with parametric and semiparametric estimators are given. This method and standard ones are applied to three small-sample empirical applications of the wage-participation model for female teenage heads of households, immigrants, and Native Americans.
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Bibliographic InfoPaper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 955.
Length: 41 pages
Date of creation: 2001
Date of revision:
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Postal: University of California, Giannini Foundation of Agricultural Economics Library, 248 Giannini Hall #3310, Berkeley CA 94720-3310
Other versions of this item:
- Amos Golan & Enrico Moretti & Jeffrey M.Perloff, 2004. "A Small-Sample Estimator for the Sample-Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 71-91.
- Golan, Amos & Moretti, Enrico & Perloff, Jeffrey M., 2001. "A Small-Sample Estimator for the Sample-Selection Model," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4c82d2nv, Department of Agricultural & Resource Economics, UC Berkeley.
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