Arbeitspapier
Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?
This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper Series 1 ; No. 2006,32
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Estimation: General
Forecasting Models; Simulation Methods
Bayesian Analysis: General
- Thema
-
Bayesian VAR
ridge regression
Lasso regression
principal components
large cross-sections
Prognoseverfahren
Zeitreihenanalyse
Regression
Bayes-Statistik
VAR-Modell
Theorie
Hauptkomponentenregression
- Ereignis
-
Geistige Schöpfung
- (wer)
-
De Mol, Christine
Giannone, Domenico
Reichlin, Lucrezia
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsche Bundesbank
- (wo)
-
Frankfurt a. M.
- (wann)
-
2006
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 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
- De Mol, Christine
- Giannone, Domenico
- Reichlin, Lucrezia
- Deutsche Bundesbank
Entstanden
- 2006
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