Bootstrap VAR forecasts: The effect of model uncertainties

Diego Fresoli

Journal of Forecasting, 2022, vol. 41, issue 2, 279-293

Abstract: VAR models are popular to forecast macroeconomic time series. However, the model, the parameters, and the error distribution are rarely known without uncertainty, so bootstrap methods are applied to deal with these sources of uncertainties. In this paper, the performance of the popular forecast Bonferroni cubes based on the Gaussian method and variants of the bootstrap procedure that incorporate error distribution, parameter uncertainty, bias correction, and lag order uncertainty are compared. Monte Carlo simulations suggest that the best performance of bootstrap cubes are obtained when the parameter uncertainty is considered, being the bias and model uncertainties important for long‐run forecast regions in persistent VAR models. Similar conclusions are found in an empirical application based on a four variate system containing US monthly percent changes of the industrial production index, the S&P500 stock market index, its dividend yield, and the unemployment rate.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.2809

Related works:
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:41:y:2022:i:2:p:279-293

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 ().