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A Method for Measuring Treatment Effects on the Treated without Randomization

Author

Listed:
  • P. A. V. B. Swamya
  • S. G. Hall
  • G. S. Tavlas
  • I. Chang
  • H. D. Gibson
  • W. H. Greene
  • J. S. Mehta

Abstract

This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal restrictions, unknown functional form and true unobserved variables such that it is a credible model of the underlying real world relationship. Subsequently, we manipulate the model in order to put it in an estimable form. In contrast to other empirical methodologies, which derive average treatment effects, we derive an analytical formula that provides estimates of treatment effects on each treated individual. We also provide an empirical example that illustrates our methodology.

Suggested Citation

  • P. A. V. B. Swamya & S. G. Hall & G. S. Tavlas & I. Chang & H. D. Gibson & W. H. Greene & J. S. Mehta, 2016. "A Method for Measuring Treatment Effects on the Treated without Randomization," Discussion Papers in Economics 16/02, Division of Economics, School of Business, University of Leicester.
    • Handle: RePEc:lec:leecon:16/02
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    References listed on IDEAS

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    10. Swamy P. A. V. B. & Tavlas George S & Hall Stephen G. F. & Hondroyiannis George, 2010. "Estimation of Parameters in the Presence of Model Misspecification and Measurement Error," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-35, May.
    11. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
    12. Jun Ma & Mark Wohar (ed.), 2014. "Recent Advances in Estimating Nonlinear Models," Springer Books, Springer, edition 127, number 978-1-4614-8060-0, April.
    13. Granger Clive W.J., 2008. "Non-Linear Models: Where Do We Go Next - Time Varying Parameter Models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-11, September.
    14. Swamy, P.A.V.B. & Mehta, J.S. & Tavlas, G.S. & Hall, S.G., 2015. "Two applications of the random coefficient procedure: Correcting for misspecifications in a small area level model and resolving Simpson's paradox," Economic Modelling, Elsevier, vol. 45(C), pages 93-98.
    15. George, Edward I. & Sun, Dongchu & Ni, Shawn, 2008. "Bayesian stochastic search for VAR model restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 553-580, January.
    16. I-Lok Chang & P.A.V.B. Swamy & Charles Hallahan & George S. Tavlas, 2000. "A Computational Approach to Finding Causal Economic Laws," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 105-136, October.
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    Cited by:

    1. P.A.V.B. Swamy & Jatinder S. Mehta & I-Lok Chang, 2017. "Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models," Econometrics, MDPI, vol. 5(1), pages 1-17, February.
    2. Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2017. "Self-fulfilling dynamics: The interactions of sovereign spreads, sovereign ratings and bank ratings during the euro financial crisis," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 371-385.

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    More about this item

    Keywords

    Causality; Real-world relationship; Unique error term; Treatment effect; Non-experimental situation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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