Smooth Forecast Reconciliation

March 22, 2024

Preview Citation

Format: Chicago

Sakai Ando. Smooth Forecast Reconciliation, (USA: International Monetary Fund, 2024) accessed December 3, 2024

Disclaimer: 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.

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.

Subject: Environment, GDP forecasting, National accounts

Keywords: Cross-sectional, Forecast performance, Forecast Reconciliation, GDP forecasting, Hodrick-Prescott filter, Minimum Trace Reconciliation, Multivariate time series, Performance comparison, Smoothness, Smoothness parameter, Temporal

Publication Details

Pages:

28
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2024/066
Stock No:

WPIEA2024066
ISBN:

9798400268922
ISSN:

1018-5941

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