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A Stata package for the application of semiparametric estimators of dose-response functions

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  • BIA Michela
  • FLORES Carlos A.
  • FLORES-LAGUNES Alfonso
  • MATTEI Alessandra

Abstract

In many observational studies the treatment may not be binary or categorical, but rather continuous in nature, so focus is on estimating a continuous dose-response function. In this paper we propose a set of Stata programs to semiparametrically estimate the dose-response function of a continuous treatment, under the key assumption that adjusting for pre-treatment variables removes all biases (uncounfoundedness). We focus on kernel methods and penalized spline models, and use generalized propensity score methods under continuous treatment regimes for covariate adjustment. Several alternative parametric assumptions on the functional form of the generalized propensity score are implemented in our Stata programs, which also allow users to impose a common support condition and evaluate the balancing of the covariates using various approaches. We illustrate our routines by estimating the effect of the prize amount on subsequent labor earnings for Massachusetts lottery winners, using a data set collected by Imbens et al. (2001).

Suggested Citation

  • BIA Michela & FLORES Carlos A. & FLORES-LAGUNES Alfonso & MATTEI Alessandra, 2013. "A Stata package for the application of semiparametric estimators of dose-response functions," LISER Working Paper Series 2013-07, LISER.
  • Handle: RePEc:irs:cepswp:2013-07
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    References listed on IDEAS

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    1. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," IZA Discussion Papers 3255, Institute of Labor Economics (IZA).
    2. Michela Bia & Philippe Van Kerm, 2014. "Space-filling location selection," Stata Journal, StataCorp LP, vol. 14(3), pages 605-622, September.
    3. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(2), pages 1-21, June.
    4. Carlos A. Flores & Alfonso Flores-Lagunes & Arturo Gonzalez & Todd C. Neumann, 2012. "Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 153-171, February.
    5. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LP, vol. 8(3), pages 354-373, September.
    6. Michela Bia & Alessandra Mattei, 2012. "Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(4), pages 485-516, November.
    7. Maarten L. Buis & Nicholas J. Cox & Stephen P. Jenkins, 2003. "BETAFIT: Stata module to fit a two-parameter beta distribution," Statistical Software Components S435303, Boston College Department of Economics, revised 03 Feb 2012.
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    3. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
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    More about this item

    Keywords

    dose-response function; generalized propensity score; kernel estimator; penalized spline estimator; weak unconfoundedness;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J70 - Labor and Demographic Economics - - Labor Discrimination - - - General

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