Arbeitspapier
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. 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.
- Language
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Englisch
- Bibliographic citation
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Series: ISER Working Paper Series ; No. 2011-12
- Classification
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Wirtschaft
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
-
quantile regression
ecological inference
aggregation bias
- Event
-
Geistige Schöpfung
- (who)
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Nicoletti, Cheti
Best, Nicky G.
- Event
-
Veröffentlichung
- (who)
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University of Essex, Institute for Social and Economic Research (ISER)
- (where)
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Colchester
- (when)
-
2011
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Nicoletti, Cheti
- Best, Nicky G.
- University of Essex, Institute for Social and Economic Research (ISER)
Time of origin
- 2011
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