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.
- Sprache
-
Englisch
- Erschienen in
-
Series: ISER Working Paper Series ; No. 2011-12
- Klassifikation
-
Wirtschaft
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Thema
-
quantile regression
ecological inference
aggregation bias
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Nicoletti, Cheti
Best, Nicky G.
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Essex, Institute for Social and Economic Research (ISER)
- (wo)
-
Colchester
- (wann)
-
2011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Nicoletti, Cheti
- Best, Nicky G.
- University of Essex, Institute for Social and Economic Research (ISER)
Entstanden
- 2011
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