Smooth Forecast Reconciliation

Sakai Ando

No 2024/066, IMF Working Papers from International Monetary Fund

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.

Keywords: Smoothness; Forecast Reconciliation; Minimum Trace Reconciliation; Hodrick-Prescott filter; Cross-sectional; Temporal; performance comparison; smoothness parameter; forecast performance; multivariate time series; GDP forecasting (search for similar items in EconPapers)
Pages: 28
Date: 2024-03-22
New Economics Papers: this item is included in nep-ets and nep-for
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