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High-Dimensional Forecasting with Known Knowns and Known Unknowns

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  • Pesaran, M. H.
  • Smith, R. P.

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.

Suggested Citation

  • Pesaran, M. H. & Smith, R. P., 2024. "High-Dimensional Forecasting with Known Knowns and Known Unknowns," Cambridge Working Papers in Economics 2406, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2406
    Note: mhp1
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    Keywords

    Forecasting; high-dimensional data; Lasso; OCMT; latent factors; principal components;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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