Resource title

Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?

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Resource 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.

Resource author

Christine De Mol, Domenico Giannone, Lucrezia Reichlin

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Resource publish date

Resource language

eng

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text/html

Resource resource URL

http://hdl.handle.net/10419/19661

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Adapt according to the presented license agreement and reference the original author.