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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 pr...
|a Forecasting using a large number of predictors :
|b is bayesian regression a valid alternative to principal components? /
|c Christine De Mol, Domenico Giannone, Lucrezia Reichlin.
260
|a Frankfurt am Main :
|b Deutsche Bundesbank,
|c 2006
|a 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.
650
1
4
|a ANALISIS BAYESIANO
|9 5402
653
4
|a PRONOSTICOS ECONOMICOS
653
4
|a PREDICCIONES ECONOMICAS
650
4
|9 2897
|a PREVISIONES ECONOMICAS
700
1
|9 5523
|a Giannone, Domenico
700
1
|9 5524
|a Reichlin, Lucrezia,
|d 1954-
830
0
|9 4690
|a Discussion paper (Deutsche Bundesbank).
|p Series 1: economic studies ;
|v no. 32/2006
