Quantile regression with aggregated data

Cheti Nicoletti and Nicky G. Best

No 2011-12, ISER Working Paper Series from Institute for Social and Economic Research

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.

Date: 2011-05-13
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Journal Article: Quantile regression with aggregated data (2012)
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