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2011 | 3 | nr 2 | 97--110
Tytuł artykułu

Dynamic Caliper Matching

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Matched sampling is a methodology used to estimate treatment effects. A caliper mechanism is used to achieve better similarity among matched pairs. We investigate finite sample properties of matching with caliper and propose a slight modification to the existing mechanism. The simulation study compare performance of both methods and show that standard caliper perform well only in case of constant treatment or uniform propensity score distribution. Secondly, in a case of non-uniform distribution and non-uniform treatment the dynamic caliper method outperform standard caliper matching. (original abstract)
Rocznik
Tom
3
Numer
Strony
97--110
Opis fizyczny
Twórcy
  • University of Warsaw, Poland
Bibliografia
  • [1] 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.
  • [2] Blundell R., Costa-Diás M. (2009) Alternative Approaches to Evaluation in Empirical Microeconometrics, Journal of Human Resources, vol. 44, pp. 565-640.
  • [3] Cochrane W., Rubin D. (1973) Controling Bias in Observational Studies. A Review, Sankhya, vol. 35, pp. 417-466.
  • [4] Frölich (2004) Finite Sample Properties of Propensity-score Matching and Weighting Estimators, The Review of Economics and Statistics 86, 77-90.
  • [5] Imbens G. (2004) Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review, Review of Economics and Statistics 86, 4-29.
  • [6] 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 64, 605-654.
  • [7] Lee M-J. (2005) Micro-Econometrics for Policy, Program, and Treatment Effects, Oxford University Press.
  • [8] Rosenbaum P. (1985) Optimal Matching for Observational Studies, Journal of the American Statistical Association 84, 1024-1032.
  • [9] Rosenbaum P., Rubin D. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika 70, 41-55.
  • [10] Rosenbaum P., Rubin D. (1985) Constructing Control Group using Multivariate Matched Sampling Methods that Incorporate Propensity Score, The American Statistician 39, 33-38.
  • [11] Rubin D. (1973) Matching to Remove Bias in Observational Studies, Biometrics 29, 159-183.
  • [12] Rubin (1980) Bias Reduction using Mahalanobis Metric Matching, Biometrics 36, 293-298.
  • [13] Rubin D., Thomas N. (2000) Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates, Journal of American Statistical Association 95, 573-585.
  • [14] Smith J., Todd P. (2005) Does Matching Overcome LaLonde's Critique of nonexperimental estimators?, Journal of Econometrics, vol. 125, str. 305-353.
  • [15] Todd P. (2006) Matching estimators, unpublished manuscript.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.ekon-element-000171231415

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