Smooth Forecast Reconciliation
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Summary:
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.
Series:
Working Paper No. 2024/066
Subject:
Environment GDP forecasting National accounts
Frequency:
regular
English
Publication Date:
March 22, 2024
ISBN/ISSN:
9798400268922/1018-5941
Stock No:
WPIEA2024066
Format:
Paper
Pages:
28
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