Quantile regression with aggregated data
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.Â 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 of creation:||13 May 2011|
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- Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2003. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," Working Papers 109, RAND Corporation Publications Department.
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- Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
- Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001.
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- Omar Arias & Kevin F. Hallock & Walter Sosa Escudero, 1999. "Individual Heterogeneity in the Returns to Schooling: Instrumental Variables Quantile Regression using Twins Data," Department of Economics, Working Papers 016, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
- Rothe, Christoph, 2010. "Identification of unconditional partial effects in nonseparable models," Economics Letters, Elsevier, vol. 109(3), pages 171-174, December.
- SErgio Firpo & Nicole M. Fortin & Thomas Lemieux, 2006.
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Textos para discussão
533, Department of Economics PUC-Rio (Brazil).
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- Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
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