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HIGH-DIMENSIONAL FORECASTING WITH KNOWN KNOWNS AND KNOWN UNKNOWNS

Published online by Cambridge University Press:  16 October 2024

M. Hashem Pesaran  and
Ron P. Smith
M. Hashem Pesaran*
Affiliation:
University of Southern California, Los Angeles, CA, USA Trinity College, Cambridge, UK
Ron P. Smith
Affiliation:
Birkbeck, University of London, London, UK
*
Corresponding author: M. Hashem Pesaran; Email: mhp1@cam.ac.uk
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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.

Keywords

forecastinghigh-dimensional dataLassoOCMTlatent factorsprincipal componentsC53C55E37E52
Type
Lecture
Information
National Institute Economic Review , Volume 267 , Spring 2024 , pp. 1 - 25
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of National Institute Economic Review

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