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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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    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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