Quantile regression with aggregated data
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.Download Info
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Paper provided by Institute for Social and Economic Research in its series ISER Working Paper Series with number 2011-12.Length:
Date of creation: 13 May 2011
Date of revision:
Publication status: published
Handle: RePEc:ese:iserwp:2011-12
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Related research
Keywords:Other versions of this item:
- Nicoletti, Cheti & Best, Nicky, 2012. "Quantile regression with aggregated data," Economics Letters, Elsevier, vol. 117(2), pages 401-404.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-24 (All new papers)
- NEP-ECM-2011-05-24 (Econometrics)
References
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