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.
- Language
-
Englisch
- Bibliographic citation
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Series: ECB Working Paper ; No. 700
- Classification
-
Wirtschaft
Bayesian Analysis: General
Estimation: General
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Forecasting Models; Simulation Methods
- Subject
-
Bayesian VAR
large cross-sections
Lasso regression
principal components
ridge regression
Prognoseverfahren
Zeitreihenanalyse
Regressionsanalyse
Bayes-Statistik
VAR-Modell
Theorie
- Event
-
Geistige Schöpfung
- (who)
-
De Mol, Christine
Giannone, Domenico
Reichlin, Lucrezia
- Event
-
Veröffentlichung
- (who)
-
European Central Bank (ECB)
- (where)
-
Frankfurt a. M.
- (when)
-
2006
- Handle
- Last update
-
10.03.2025, 11:42 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
- De Mol, Christine
- Giannone, Domenico
- Reichlin, Lucrezia
- European Central Bank (ECB)
Time of origin
- 2006
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