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Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? Author info | Abstract | Publisher info | Download info | Related research | Statistics De Mol, Christine
Giannone, Domenico
Reichlin, Lucrezia
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This paper considers Bayesian regression with normal and double exponential 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.
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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number
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Date of creation: Sep 2006Date of revision:
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Keywords: Bayesian VAR ; large cross-sections ; Lasso regression ; principal components ; ridge regressions ; Other versions of this item:
Paper De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006.
"Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? ,"
Discussion Paper Series 1: Economic Studies
2006,32, Deutsche Bundesbank, Research Centre.
[Downloadable!] Christine De Mol & Domenico Giannone & Lucrezia Reichlin, 2006.
"Forecasting using a large number of predictors - Is Bayesian regression a valid alternative to principal components? ,"
Working Paper Series
700, European Central Bank.
[Downloadable!] Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
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