Bayesian VAR forecasts, survey information, and structural change in the euro area

Gergely Ganics and Florens Odendahl

International Journal of Forecasting, 2021, vol. 37, issue 2, 971-999

Abstract: We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into the predictions of a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon but their possible paths over several horizons jointly since the survey information comes in the form of one- and two-year-ahead expectations. As well as improving the accuracy of the variable that we target, the spillover effects on “other-than-targeted” variables are relevant in size and are statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis, and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.

Keywords: Macroeconomic forecasting; Structural change; Multivariate time series; Probability forecasting; Entropic tilting (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207020301722
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Bayesian VAR forecasts, survey information and structural change in the euro area (2019)
Working Paper: Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area (2019)
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:eee:intfor:v:37:y:2021:i:2:p:971-999

DOI: 10.1016/j.ijforecast.2020.11.001

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().