Smooth Forecast Reconciliation

Author/Editor:

Sakai Ando

Publication Date:

March 22, 2024

Electronic Access:

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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:

Frequency:

regular

English

Publication Date:

March 22, 2024

ISBN/ISSN:

9798400268922/1018-5941

Stock No:

WPIEA2024066

Format:

Paper

Pages:

28

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