Was there a riverside miracle? A hierarchical framework for evaluating programs with grouped data

Journal of Business and Economic Statistics

Volumn 21, Issue 1, 2003, Pages 1-11

  Dehejia, Rajeev H ^a,b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b NATIONAL BUREAU OF ECONOMIC RESEARCH   (United States)

Author keywords

Bayesian methods; Predictive uncertainty; Site effects

Indexed keywords

EID: 0037237264     PISSN: 07350015     EISSN: None     Source Type: Journal    
DOI: 10.1198/073500102288618702     Document Type: Article

References (24)

1. Albert, J., and Chib, S. (1993), "Bayesian Analysis of Binary and Polychotomous Response Data" Journal of the American Statistical Association, 88, 669-679.
2. Card, D., and Krueger, A. (1992), "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States," Journal of Political Economy, 100, 1-40.
3. Chamberlain, G., and Imbens, G. (1996), "Hierarchical Bayes Models With Many Instrumental Variables," Paper 1781, Harvard Institute of Economic Research.
4. Chib, S. (1992), "Bayes Inference in the Tobit Censored Regression Model," Journal of Econometrics, 51, 79-99.
5. Chib, S., and Greenberg, E. (1994), "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, 12, 409-431.
6. Cooper, H., and Hedges, L. (eds.) (1994), The Handbook of Research Synthesis, New York: Russell Sage.
7. Dehejia, R. (1999), "Program Evaluation as a Decision Problem," Working Paper 6954, National Bureau Of Economic Research, forthcoming Journal of Econometrics.
8. _, and Wahba, S. (2002), "Propensity Score-Matching Methods for Non Experimental Causal Studies," Working Paper 6829, National Bureau of Economic Research, Review of Economics and Statistics, 84, 151-161.
9. _ (1999), "Causal Effects in Non-Experimental Studies: Reevaluating the Evaluation of Training Programs," Journal of the American Statistical Association, 94, 1053-1062.
10. Gelfand, A. E., and Smith, A. F. M. (1990), "Sampling-Based Approaches to Calculating Marginal Densities," Journal of the American Statistical Association, 85, 398-409.
11. Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1996), Bayesian Data Analysis, London: Chapman and Hall.
12. Geman, S., and Geman, D. (1984), "Stochastic Relaxation, Gibbs Distributions, and Bayesian Restoration of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
13. Geweke, J., and Keane, M. (1996), "An Empirical Analysis of the Male Income Dynamics in the PSID: 1968-1989," Journal of Econometrics, 96, 293-356.
14. Heckman, J., and Smith, J. (1996), "The Sensitivity of Experimental Impact Estimates: Evidence from the National JTPA Study," unpublished manuscript, University of Western Ontario.
15. Heckman, J., Ichimura, H., and Todd, P. (1997), "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, 64, 605-654.
16. _ (1998), "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, 65, 261-294.
17. Hotz, V., Imbens, G., and Klerman, J. (2000), "The Long-Term Gains From GAIN: A Re-Analysis of the Impacts of the California GAIN Program," unpublished manuscript, University of California Los Angeles.
18. Hotz, V. J., Imbens, G., and Mortimer, J. (1999), "Predicting the Efficacy of Future Training Programs Using Past Experiences," Technical Working Paper 238, National Bureau of Economic Research.
19. Nelson, D. (1997), "Some 'Best Practices' and 'Most Promising Models' for Welfare Reform," memorandum, Annie E. Casey Foundation, Baltimore, http://center.hamline.edu/mcknight/caseymemo.htm.
20. Riccio, J., Friedlander, D., and Freedman, S. (1994), GAIN: Benefits, Costs, and Three-Year Impacts of a Welfare-to-Work Program, New York: Manpower Demonstration Research Corporation.
21. Rosenbaum, P., and Rubin, D. (1983), "The Central Role of the Propensity Score in Observational Studies for Causal Effects," Biometrika, 70, 41-55.
22. _ (1985), "Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score," The American Statistician, 39, 33-38.
23. Rossi, P., McCulloch, R., and Allenby, G. (1995), "Hierarchical Modeling of Consumer Heterogeneity: An Application to Target Marketing," in Case Studies in Bayesian Statistics, Vol. II, eds. C. Gatsonis, J. Hodges, R. Kass, and N. Singpurwalla, New York: Springer-Verlag.
24. Tanner, M., and Wong, W. (1987), "The Calculation of Posterior Distributions by Data Augmentation," Journal of the American Statistical Association, 82, 528-550.