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Estimating the dose-response function through the GLM approach

Author

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  • Guardabascio, Barbara
  • Ventura, Marco

Abstract

This paper revises the estimation of the dose-response function as in Hirano and Imbens (2004) by proposing a flexible way to estimate the generalized propensity score when the treatment variable is not necessarily normally distributed. We also provide a set of programs that accomplish this task by using the GLM in the first step of the computation.

Suggested Citation

  • Guardabascio, Barbara & Ventura, Marco, 2013. "Estimating the dose-response function through the GLM approach," MPRA Paper 45013, University Library of Munich, Germany, revised 13 Mar 2013.
  • Handle: RePEc:pra:mprapa:45013
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    File URL: https://mpra.ub.uni-muenchen.de/45013/1/MPRA_paper_45013.pdf
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    References listed on IDEAS

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    1. Liu, Jin-Long & Liu, Jin-Tan & Hammitt, James K. & Chou, Shin-Yi, 1999. "The price elasticity of opium in Taiwan, 1914-1942," Journal of Health Economics, Elsevier, vol. 18(6), pages 795-810, December.
    2. Helmut Fryges & Joachim Wagner, 2008. "Exports and Productivity Growth: First Evidence from a Continuous Treatment Approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 144(4), pages 695-722, December.
    3. Helmut Fryges, 2009. "The export-growth relationship: estimating a dose-response function," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1855-1859.
    4. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, April.
    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. Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 01 Feb 2018.
    7. 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.
    8. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    9. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    10. Hausman, Jerry A & Leonard, Gregory K, 1997. "Superstars in the National Basketball Association: Economic Value and Policy," Journal of Labor Economics, University of Chicago Press, vol. 15(4), pages 586-624, October.
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    Cited by:

    1. repec:spr:ssefpa:v:9:y:2017:i:6:d:10.1007_s12571-017-0730-y is not listed on IDEAS
    2. Chepchirchir, R. & Macharia, I. & Murage, A.W. & Midega, C.A.O. & Khan, Z.R., 2016. "Impact assessment of push-pull technology on incomes, productivity and poverty among smallholder households in Eastern Uganda," 2016 AAAE Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246316, African Association of Agricultural Economists (AAAE).
    3. Smale, Melinda & Kusunose, Yoko & Mathenge, Mary K. & Alia, Didier, 2014. "Destination or Distraction? Querying the Linkage between Off-farm Income and Farm Investments in Kenya," Food Security International Development Working Papers 196829, Michigan State University, Department of Agricultural, Food, and Resource Economics.

    More about this item

    Keywords

    generalized propensity score; GLM; dose-response; continuous treatment; bias removal;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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