Multi-step Forecasting with Large Vector Autoregressions

Andreas Pick and Matthijs Carpay

A chapter in Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, 2022, vol. 43A, pp 73-98 from Emerald Group Publishing Limited

Abstract: This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.

Keywords: Multi-step forecasting; VAR; dimension reduction; density forecasting; time varying parameters (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 1-90532021000043A005
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

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:eme:aecozz:s0731-90532021000043a005

DOI: 10.1108/S0731-90532021000043A005

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().