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Smooth Forecast Reconciliation

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  • Mr. Sakai Ando

Abstract

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.

Suggested Citation

  • Mr. Sakai Ando, 2024. "Smooth Forecast Reconciliation," IMF Working Papers 2024/066, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2024/066
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