Principal Stratification in sample selection problems with non normal error terms
The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true distribution of the error terms with a mixture of Gaussian. We propose an EM type algorithm for ML estimation. In a simulation study we show that our estimator has lower MSE than the ML and two-step Heckman estimators with any non normal distribution considered for the error terms. Finally we provide an application to the Job Corps training program.
|Date of creation:||02 May 2011|
|Date of revision:||02 May 2011|
|Contact details of provider:|| Postal: |
Web page: http://www.ceistorvergata.it
More information through EDIRC
|Order Information:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
Web: http://www.ceistorvergata.it Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Newey, Whitney K & Powell, James L & Walker, James R, 1990.
"Semiparametric Estimation of Selection Models: Some Empirical Results,"
American Economic Review,
American Economic Association, vol. 80(2), pages 324-28, May.
- Newey, W.K. & Powell, J.L. & Walker, J.R., 1990. "Semiparametric Estimation Of Selection Models: Some Empirical Results," Working papers 9001, Wisconsin Madison - Social Systems.
- Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
- Zhang, Junni L. & Rubin, Donald B. & Mealli, Fabrizia, 2009. "Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 166-176.
- Mealli, Fabrizia & Pacini, Barbara, 2008. "Comparing principal stratification and selection models in parametric causal inference with nonignorable missingness," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 507-516, December.
- Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
- Bartolucci, F. & Scaccia, L., 2005. "The use of mixtures for dealing with non-normal regression errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 821-834, April.
When requesting a correction, please mention this item's handle: RePEc:rtv:ceisrp:194. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Barbara Piazzi)
If references are entirely missing, you can add them using this form.