Please use this identifier to cite or link to this item:
http://dspace.mediu.edu.my:8181/xmlui/handle/10419/19661| Title: | Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? |
| Keywords: | C33 C13 C53 C11 ddc:330 Bayesian VAR ridge regression Lasso regression principal components large cross-sections Prognoseverfahren Zeitreihenanalyse Regression Bayes-Statistik VAR-Modell Theorie Hauptkomponentenregression |
| Issue Date: | 16-Oct-2013 |
| Description: | 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/10419/19661 |
| Other Identifiers: | http://hdl.handle.net/10419/19661 ppn:519034066 RePEc:zbw:bubdp1:5040 |
| Appears in Collections: | EconStor |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
