Philippe Goulet Coulombe, Karin Klieber, Christophe Barrette, Maximilian Goebel
arXiv 8 Apr 2024 · Econometrics
arXiv:2404.05209 · PDF · DOI · OpenAlex · Extracted main text
Timely monetary policy decision-making requires timely core inflation measures. We create a new core inflation series that is explicitly designed to succeed at that goal. Precisely, we introduce the Assemblage Regression, a generalized nonnegative ridge regression problem that optimizes the price index's subcomponent weights such that the aggregate is maximally predictive of future headline inflation. Ordering subcomponents according to their rank in each period switches the algorithm to be learning supervised trimmed inflation - or, put differently, the maximally forward-looking summary statistic of the realized price changes distribution. In an extensive out-of-sample forecasting experiment for the US and the euro area, we find substantial improvements for signaling medium-term inflation developments in both the pre- and post-Covid years. Those coming from the supervised trimmed version are particularly striking, and are attributable to a highly asymmetric trimming which contrasts with conventional indicators. We also find that this metric was indicating first upward pressures on inflation as early as mid-2020 and quickly captured the turning point in 2022. We also consider extensions, like assembling inflation from geographical regions, trimmed temporal aggregation, and building core measures specialized for either upside or downside inflation risks.
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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 | Hamilton, J. D (2018) Why You Should Never Use the Hodrick-Prescott Filter | 0.811 | 4 | 2 | 100% |
| 2 | Stock, J. H. and Watson, M. W (2016) Core Inflation and Trend Inflation | 0.811 | 4 | 2 | 100% |
| 3 | Bańbura, M. and Bobeica, E (2020) PCCI – a Data-Rich Measure of Underlying Inflation in the Euro Area | 0.737 | 3 | 2 | 100% |
| 4 | Bils, M. and Klenow, P. J (2004) Some evidence on the importance of sticky prices | 0.737 | 3 | 2 | 100% |
| 5 | Leduc, S., Wilson, D. J., and Zhao, C (2023) Will a cooler labor market slow supercore inflation? | 0.737 | 3 | 2 | 100% |
| 6 | McAleer, M. and Medeiros, M. C (2008) Realized volatility: A review | 0.737 | 3 | 2 | 100% |
| 7 | Wang, X., Hyndman, R. J., Li, F., and Kang, Y (2023) Forecast combinations: an over 50-year review | 0.737 | 3 | 2 | 100% |
| 8 | Bilke, L. and Stracca, L (2007) A Persistence-Weighted Measure of Core Inflation in the Euro Area | 0.644 | 2 | 2 | 100% |
| 9 | Cogley, T (2002) A Simple Adaptive Measure of Core Inflation | 0.644 | 2 | 2 | 100% |
| 10 | Bryan, M. F., Cecchetti, S. G., and Wiggins, R. L (1997) Efficient inflation estimation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 98 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0.29cm 22.5524 dpd Ordinary Least Squares as an Attention Mechanism . 0.25cm | 0.405 | 1 | 1 |