Philippe Goulet Coulombe, Karin Klieber
arXiv 22 Jan 2025 · Econometrics · publishedEconomics Letters (2025)
arXiv:2501.13222 · PDF · DOI · OpenAlex · Extracted main text
The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as 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 economic conditions. Too narrow averages are noisy, limiting signal extraction capabilities. As is well known, this is a bias-variance trade-off. However, it is a time-varying one: the optimal size of the 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 series such as headline inflation, core inflation, and real activity indicators. Notably, we find that this simple tool provides a different account of the post-pandemic inflation acceleration and subsequent deceleration.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stock, J. H. and Watson, M. W (2007) Why has us inflation become harder to forecast? | 0.830 | 7 | 3 | 57% |
| 2 | Goulet Coulombe, P (2024) The macroeconomy as a random forest | 0.644 | 2 | 2 | 100% |
| 3 | Eeckhout, J (2023) Instantaneous inflation | 0.644 | 2 | 2 | 100% |
| 4 | Lin, Y. and Jeon, Y (2006) Random forests and adaptive nearest neighbors | 0.644 | 2 | 2 | 100% |
| 5 | Kim, S.-J., Koh, K., Boyd, S., and Gorinevsky, D (2009) $_1$ trend filtering | 0.511 | 2 | 2 | 50% |
| 6 | Phillips, P. C. and Shi, Z (2021) Boosting: Why you can use the hp filter | 0.511 | 2 | 2 | 50% |
| 7 | Savitzky, A. and Golay, M. J (1964) Smoothing and differentiation of data by simplified least squares procedures | 0.511 | 2 | 2 | 50% |
| 8 | Tibshirani, R. J. and Taylor, J (2011) The solution path of the generalized lasso | 0.511 | 2 | 2 | 50% |
| 9 | Goulet Coulombe, P., Göbel, M., and Klieber, K (2024) Dual interpretation of machine learning forecasts self | 0.405 | 1 | 1 | 100% |
| 10 | Hamilton, J. D (2018) Why You Should Never Use the Hodrick-Prescott Filter | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 30 scored citations.