High-Dimensional Forecasting with Known Knowns and Known Unknowns

Mohammad Pesaran and Ronald Smith

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, 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: Forecasting; high-dimensional data; Lasso; OCMT; latent factors; principal components (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 E52 (search for similar items in EconPapers)
Date: 2024-02-06
New Economics Papers: this item is included in nep-for
Note: mhp1
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Related works:
Working Paper: High-dimensional forecasting with known knowns and known unknowns (2024)
Working Paper: High-Dimensional Forecasting with Known Knowns and Known Unknowns (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2406

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