Dynamic Ordering Learning in Multivariate Forecasting

Bruno P. C. Levy () and Hedibert F. Lopes ()
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Bruno P. C. Levy: Insper
Hedibert F. Lopes: Insper

A chapter in Time Series and Wavelet Analysis, 2024, pp 81-109 from Springer

Abstract: Abstract In many fields where the main goal is to produce sequential forecasts for decision-making problems, the good understanding of the contemporaneous relations among different series is crucial for the estimation of the covariance matrix. In recent years, the modified Cholesky decomposition appeared as a popular approach to covariance matrix estimation. However, its main drawback relies on the imposition of the series ordering structure. In this work, we propose a highly flexible and fast method to deal with the problem of ordering uncertainty in a dynamic fashion with the use of Dynamic ordering Probabilities. We apply the proposed method in two different forecasting contexts. The first is a dynamic portfolio allocation problem, where the investor is able to learn the contemporaneous relationships among different currencies improving final decisions and economic performance. The second is a macroeconomic application, where the econometrician can adapt sequentially to new economic environments, switching the contemporaneous relations among macroeconomic variables over time.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-66398-7_5

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DOI: 10.1007/978-3-031-66398-7_5

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