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Smooth Forecast Reconciliation
Author:
Sakai Ando
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https://orcid.org/0000-0003-2785-4375
Series:
IMF Working Papers
Volume/Issue:
Volume 2024: Issue 066
Language:
English
Publication Date:
22 Mar 2024
DOI:
https://doi.org/10.5089/9798400268922.001
ISBN:
9798400268922
ISSN:
1018-5941
Pages:
28
Keywords:
Smoothness; Forecast Reconciliation; Minimum Trace Reconciliation; Hodrick-Prescott filter; Cross-sectional; Temporal; performance comparison; smoothness parameter; forecast performance; multivariate time series; GDP forecasting
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IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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How to make forecasts that (1) satisfy constraints, like accounting identities, and (2) are smooth over time? Solving this common forecasting problem manually is resource-intensive, but the existing literature provides little guidance on how to achieve both objectives. This paper proposes a new method to smooth mixed-frequency multivariate time series subject to constraints by integrating the minimum-trace reconciliation and Hodrick-Prescott filter. With linear constraints, the method has a closed-form solution, convenient for a high-dimensional environment. Three examples show that the proposed method can reproduce the smoothness of professional forecasts subject to various constraints and slightly improve forecast performance.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund