High-dimensional forecasting with known knowns and known unknowns

Mohammad Pesaran and Ronald Smith

Papers from arXiv.org

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. 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: 2024-01, Revised 2024-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Published in NIER 267 (2024) 1-25

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http://arxiv.org/pdf/2401.14582 Latest version (application/pdf)

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:arx:papers:2401.14582

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