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An adaptive moving average for macroeconomic monitoring

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  • Goulet Coulombe, Philippe
  • Klieber, Karin

Abstract

The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series like inflation. The choice of the look-back window is crucial. Too long of a moving average is not timely enough when faced with rapidly evolving conditions. Too narrow averages are noisy, limiting signal extraction capabilities. This is a time-varying bias–variance trade-off: the optimal look-back window depends on current macroeconomic conditions. In this paper, we introduce a simple adaptive moving average estimator based on a Random Forest using as sole predictor a time trend. Then, we compare the narratives inferred from the new estimator to those derived from common alternatives across key macroeconomic indicators.

Suggested Citation

  • Goulet Coulombe, Philippe & Klieber, Karin, 2026. "An adaptive moving average for macroeconomic monitoring," Economics Letters, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:ecolet:v:259:y:2026:i:c:s016517652500610x
    DOI: 10.1016/j.econlet.2025.112773
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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