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ISER Working Paper Series 2011-12

Quantile regression with aggregated data

Authors

Publication date

13 May 2011

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.

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Related publications

  1. Quantile regression with aggregated data

    Cheti Nicoletti and Nicky Best

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