Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2011 | 12 | 3 | 597-607

Article title

An Improvement of Quality of Statistical Matching for Survey Data Using Dynamic Caliper

Content

Title variants

Languages of publication

EN

Abstracts

EN
Nowadays, matching is a widely used technique to estimate program net effects. The goal of the method is to establish a counterfactual state by choosing from the control pool a group that is similar to those in the treatment group. In this article we propose a modification of the matching with caliper procedure. The novelty in our approach is setting the caliper value as a fraction of estimated propensity score. The simulation results and examples are presented. Using Deheija and Wahba (1999) data benefits of the proposed approach are stressed. The obtained results indicate that proposed approach is more efficient than the one traditionally used.

Year

Volume

12

Issue

3

Pages

597-607

Physical description

Contributors

References

  • AUSTIN P. (2009) „Some methods of Propensity Score Matching Had Superior Performance to Others: Result of an Empirical Investigation and Monte Carlo Simulations.”, Biometrical Journal, vol. 5, pp. 171-184.
  • BLUNDELL R., COSTA-DIÁS M. (2000) „Evaluation Methods for Non-Experimental Data”, Fiscal Studies, vol. 21/4, pp. 427-468.
  • COCHRANE, RUBIN (1973) „Controling Bias in Observational Studies. A Review”, Sankhya, vol. 35, pp. 417-466.
  • DEHEJIA R., WAHBA S. (1999) „Causal Effects in Nonexperimental studies: Reevaluating the Evaluation of Training Program”, Journal of American Statistical Association, vol. 94, no 448.
  • DEHEJIA R., WAHBA S. (2002) „Propensity score matching methods for nonexperimental causal studies”, Journal of the American Statistical Association, vol. 84, pp. 151-161.
  • HECKMAN J., HOTZ J. (1989) „Choosing among alternative nonexperimental methods for estimating the impact of social programs: the case of manpower training”, Journal of the American Statistical Association, vol. 84(408), pp. 862-880.
  • HECKMAN J., ICHIMURA H., TODD P. (1997) „Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme”, The Review of Economic Studies, vol. 64/4, pp. 605-654.
  • LEE M-J. (2005) „Micro-Econometrics for Policy, Program, and Treatment Effects”, Oxford University Press.
  • ROSENBAUM P., RUBIN D. (1983) „The Central Role of the Propensity Score in Observational Studies for Causal Effects“, Biometrika, vol. 70/1, pp. 41-55.
  • RUBIN D. (1973) „Matching to Remove Bias in Observational Studies”, Biometrics, vol. 29, pp. 159-183.
  • SMITH J., TODD P. (2005) „Does Matching Overcome LaLonde’s Critique of nonexperimental estimators?”, Journal of Econometrics, vol. 125, str. 305-353.
  • STRAWIŃSKI P. (2007) „Causality, selection and engodenuous effects”[Przyczynowość, selekcja i endogeniczne oddziaływanie], Przegląd Statystyczny nr 4/2007, pp. 49-61.
  • STRAWIŃSKI P. (2009) „Matching with Dynamic Caliper. Preliminary Results”[Łączenie danych z dynamicznym obcięciem. Wyniki wstępne], Metody Ilościowe w Badaniach Ekonomicznych X, pp. 232-242.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-b7d9566b-db4f-42a8-91cd-7bb6539d872b
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.