Quantile regression with aggregated data

Publication type

ISER Working Paper Series

Series Number

2011-12

Series

ISER Working Paper Series

Authors

Publication date

May 13, 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.

Subject

Paper download  

#519967

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