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

Author

Listed:
  • Michela Bia

    () (CEPS/INSTEAD)

  • Carlos A. Flores

    () (California Polytechnic State University)

  • Alfonso Flores-Lagunes

    () (State University of New York, Binghamton)

  • Alessandra Mattei

    () (University of Florence)

Abstract

In many observational studies, the treatment may not be binary or categorical but rather continuous, so the focus is on estimating a continuous dose– response function. In this article, we propose a set of programs that semiparametrically estimate the dose–response function of a continuous treatment under the unconfoundedness assumption. We focus on kernel methods and penalized spline models and use generalized propensity-score methods under continuous treatment regimes for covariate adjustment. Our programs use generalized linear models to estimate the generalized propensity score, allowing users to choose between alternative parametric assumptions. They also allow users to impose a common support condition and evaluate the balance 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 data collected by Imbens, Rubin, and Sacerdote (2001, American Economic Review, 778–794). Copyright 2014 by StataCorp LP.

Suggested Citation

  • Michela Bia & Carlos A. Flores & Alfonso Flores-Lagunes & Alessandra Mattei, 2014. "A Stata package for the application of semiparametric estimators of dose–response functions," Stata Journal, StataCorp LP, vol. 14(3), pages 580-604, September.
  • Handle: RePEc:tsj:stataj:v:14:y:2014:i:3:p:580-604
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    References listed on IDEAS

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    1. repec:ags:stataj:122599 is not listed on IDEAS
    2. 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).
    3. Sabrina Dorn, 2012. "pscore2: Stata module to enforce balancing score property in each covariate dimension," United Kingdom Stata Users' Group Meetings 2012 11, Stata Users Group.
    4. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    5. Bia, Michela & Van Kerm, Philippe, 2014. "Space-filling location selection," Stata Journal, StataCorp LP, vol. 14(3).
    6. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 1-21, June.
    7. 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.
    8. Guido W. Imbens & Donald B. Rubin & Bruce I. Sacerdote, 2001. "Estimating the Effect of Unearned Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players," American Economic Review, American Economic Association, vol. 91(4), pages 778-794, September.
    9. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    10. Guardabascio, Barbara & Ventura, Marco, 2014. "Estimating the dose–response function through a generalized linear model approach," Stata Journal, StataCorp LP, vol. 14(1).
    11. 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.
    12. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    13. 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.
    14. 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.
    15. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    16. repec:ags:stataj:116022 is not listed on IDEAS
    17. Becker, Sascha O. & Ichino, Andrea, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 1-20.
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    Cited by:

    1. Steckenleiter, Carina & Lechner, Michael & Pawlowski, Tim & Schüttoff, Ute, 2019. "Do local public expenditures on sports facilities affect sports participation in Germany?," Economics Working Paper Series 1905, University of St. Gallen, School of Economics and Political Science.
    2. repec:gam:jsusta:v:11:y:2019:i:7:p:2158-:d:221779 is not listed on IDEAS
    3. Chiara Bocci & Marco Mariani, 2015. "L’approccio delle funzioni dose-risposta per la valutazione di trattamenti continui nei sussidi alla r&s," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2015(3 Suppl.), pages 81-102.
    4. repec:eee:ecosta:v:8:y:2018:i:c:p:13-36 is not listed on IDEAS
    5. repec:sav:ebooks:003 is not listed on IDEAS
    6. Tamru, Seneshaw & Minten, Bart, 2018. "Investing in wet mills and washed coffee in Ethiopia: Benefits and constraints," ESSP working papers 121, International Food Policy Research Institute (IFPRI).
    7. repec:bla:germec:v:19:y:2018:i:1:p:94-112 is not listed on IDEAS
    8. Mehdi Chowdhury & Dragana Radicic, 2018. "Remittances and asset accumulation in Bangladesh: A study using generalized propensity score," Discussion Papers 2018-05, University of Nottingham, CREDIT.
    9. Roberto ESPOSTI, 2014. "To match, not to match, how to match: Estimating the farm-level impact of the CAP-first pillar reform (or: How to Apply Treatment-Effect Econometrics when the Real World is;a Mess)," Working Papers 403, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Zoltán Bakucs & Imre Fertő & Zsófia Benedek, 2019. "Success or Waste of Taxpayer Money? Impact Assessment of Rural Development Programs in Hungary," Sustainability, MDPI, Open Access Journal, vol. 11(7), pages 1-23, April.
    11. Christopher Baum & Giovanni Cerulli, CNR-IRCrES, 2016. "Estimating a dose-response function with heterogeneous response to confounders when treatment is continuous and endogenous," EcoMod2016 9388, EcoMod.
    12. Davide Dottori & Caterina Giannetti, 2017. "The effect of time preferences on altruism," Discussion Papers 2017/226, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

    More about this item

    Keywords

    drf; dose–response function; generalized propensity score; kernel estimator; penalized spline estimator; weak unconfoundedness;

    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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