HIGH-DIMENSIONAL FORECASTING WITH KNOWN KNOWNS AND KNOWN UNKNOWNS

M. Hashem Pesaran and Ron P. Smith

National Institute Economic Review, 2024, vol. 267, 1-25

Abstract: Forecasts play a central role in decision-making under uncertainty. After a brief review of the general issues, this article considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and One Covariate at a time Multiple Testing, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches to forecasting. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1–2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

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:cup:nierev:v:267:y:2024:i::p:1-25_1

Access Statistics for this article

More articles in National Institute Economic Review from National Institute of Economic and Social Research Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK. Contact information at EDIRC.
Bibliographic data for series maintained by Kirk Stebbing ().