Economic Time Series Predictions and the Illusion of Support Recovery

36 Pages Posted: 30 Jan 2022 Last revised: 19 May 2022

See all articles by Philipp Adämmer

Philipp Adämmer

Helmut Schmidt University Hamburg - Department of Mathematics and Statistics

Rainer Alexander Schüssler

University of Rostock - Department of Economics

Date Written: January 27, 2022

Abstract

We investigate whether forecast algorithms advanced in statistics and econometrics are capable of detecting the number and identities of relevant predictors for economic time series. Further, we study the relation between support recovery properties and point predictive accuracy. A novel feature of our approach is that we link results from empirical studies with simulation analyses by using the degree of predictability inferred via the former to pin down realistic signal-to-noise ratios for the simulations. While methods that combine feature selection and shrinkage exhibit good support recovery properties in low noise environments, none of the methods unveils the true number and identities of relevant predictors in realistic settings. Nevertheless, producing useful point forecasts is possible, especially when using forecast combinations.

Keywords: Feature selection, Signal-to-noise ratio, Shrinkage, Forecast combinations

JEL Classification: C53, C55

Suggested Citation

Adämmer, Philipp and Schüssler, Rainer Alexander, Economic Time Series Predictions and the Illusion of Support Recovery (January 27, 2022). Available at SSRN: https://ssrn.com/abstract=4019646 or http://dx.doi.org/10.2139/ssrn.4019646

Philipp Adämmer

Helmut Schmidt University Hamburg - Department of Mathematics and Statistics ( email )

Hostenhofweg 85
Hamburg, 22043
Germany

Rainer Alexander Schüssler (Contact Author)

University of Rostock - Department of Economics ( email )

Ulmenstr. 69
Rostock, 18057
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
59
Abstract Views
206
rank
481,980
PlumX Metrics