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Nonparametric Estimators of Dose-Response Functions

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  • BIA Michela
  • FLORES Carlos A.
  • MATTEI Alessandra

Abstract

We propose two semiparametric estimators of the dose-response function based on spline techniques. Under uncounfoundedness, the generalized propensity score can be used to estimate dose-response functions (DRF) and marginal treatment effect functions. In many observational studies treatment may not be binary or categorical. In such cases, one may be interested in estimating the dose-response function in a setting with a continuous treatment. We evaluate the performance of the proposed estimators using Monte Carlo simulation methods. The simulation results suggested that the estimated DRF is robust to the specific semiparametric estimator used, while the parametric estimates of the DRF were sensitive to model mis-specification. We apply our approach to the problem of evaluating the effect on innovation sales of Research and Development (R&D) financial aids received by Luxembourgish firms in 2004 and 2005.

Suggested Citation

  • BIA Michela & FLORES Carlos A. & MATTEI Alessandra, 2011. "Nonparametric Estimators of Dose-Response Functions," LISER Working Paper Series 2011-40, Luxembourg Institute of Socio-Economic Research (LISER).
    • Handle: RePEc:irs:cepswp:2011-40
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Giovanni Cerulli & Bianca Potì, 2014. "The Impact of Public Support Intensity on Business R&D: Evidence from a Dose-Response Approach," ERSA conference papers ersa14p625, European Regional Science Association.
    2. Giovanni Cerulli & Bianca Poti', 2016. "Explaining firm sensitivity to R&D subsidies within a dose-response model: The role of financial constraints, real cost of investment, and strategic value of R&D," DEM Working Papers 2016/09, Department of Economics and Management.
    3. Birthal, Pratap S. & Roy, Devesh & Negi, Digvijay S., 2015. "Assessing the Impact of Crop Diversification on Farm Poverty in India," World Development, Elsevier, vol. 72(C), pages 70-92.
    4. d’Artis Kancs & Boriss Siliverstovs, 2020. "Employment effect of innovation," Empirical Economics, Springer, vol. 59(3), pages 1373-1391, September.
    5. Kreif, N. & Grieve, R. & Díaz, I. & Harrison, D., 2014. "Health econometric evaluation of the effects of a continuous treatment: a machine learning approach," Health, Econometrics and Data Group (HEDG) Working Papers 14/19, HEDG, c/o Department of Economics, University of York.
    6. Birthal, Pratap S. & Roy, Devesh & Negi, Digvijay S., 2015. "Agricultural diversification and poverty in India," IFPRI discussion papers 1446, International Food Policy Research Institute (IFPRI).
    7. Giovanni Cerulli, 2014. "CTREATREG: Stata module for estimating dose-response models under exogenous and endogenous treatment," CERIS Working Paper 201405, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.

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    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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