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Quantile regression with aggregated data

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  • Nicoletti, Cheti
  • G. Best, Nicky

Abstract

Analyses using aggregated data may bias inference. In this work we show how to avoid or at least reduce this bias when estimating quantile regressions using aggregated information. This is possible by considering the unconditional quantile regression recently introduced by Firpo et al (2009) and using a specific strategy to aggregate the data.

Suggested Citation

  • Nicoletti, Cheti & G. Best, Nicky, 2011. "Quantile regression with aggregated data," ISER Working Paper Series 2011-12, Institute for Social and Economic Research.
    • Handle: RePEc:ese:iserwp:2011-12
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    References listed on IDEAS

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    1. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
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    4. Rothe, Christoph, 2009. "Unconditional Partial Effects of Binary Covariates," TSE Working Papers 09-79, Toulouse School of Economics (TSE).
    5. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    6. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Rothe, Christoph, 2010. "Identification of unconditional partial effects in nonseparable models," Economics Letters, Elsevier, vol. 109(3), pages 171-174, December.
    9. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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    Cited by:

    1. Ismail, I. & Stam, P.J.A. & Portrait, F.R.M. & van Witteloostuijn, A. & Koolman, X., 2024. "Addressing unanticipated interactions in risk equalization: A machine learning approach to modeling medical expenditure risk," Economic Modelling, Elsevier, vol. 130(C).

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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