Forecasting the production side of GDP

Gregor Bäurle, Elizabeth Steiner and Gabriel Züllig ()
Authors registered in the RePEc Author Service: Gregor Bäurle ()

Journal of Forecasting, 2021, vol. 40, issue 3, 458-480

Abstract: We evaluate the forecasting performance of time series models for the production side of gross domestic product (GDP)—that is, for the sectoral real value‐added series summing up to aggregate output. We focus on two strategies to model a large number of interdependent time series simultaneously: a Bayesian vector autoregressive model (BVAR) and a factor model structure; and compare them to simple aggregate and disaggregate benchmarks. We evaluate point and density forecasts for aggregate GDP and the cross‐sectional distribution of sectoral real value‐added growth in the euro area and Switzerland. We find that the factor model structure outperforms the benchmarks in most tests, and in many cases also the BVAR. An analysis of the covariance matrix of the sectoral forecast errors suggests that the superiority can be traced back to the ability to capture sectoral comovement more accurately.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.2725

Related works:
Working Paper: Forecasting the production side of GDP (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:3:p:458-480

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().