arXiv 15 Apr 2026 · Econometrics
arXiv:2604.14467 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the 2021 U.S. inflation forecasting failure. I show that the failure was primarily driven by sample composition rather than functional-form misspecification: estimation samples dominated by the Great Moderation underweight supply-shock regimes, and expectations anchored to that regime were slow to recognize the shift. Three historically informed adjustments, an intercept correction, a similarity re-estimation on 1970s data, and a kernel-weighted estimator, substantially close the forecast gap, and the gains extend to eight additional U.S. price indices. Household survey respondents over 60, whose lifetime includes the 1970s, reported higher inflation expectations from early 2021, consistent with experience-based learning; younger cohorts remained anchored to the prevailing regime. A controlled experiment with large language models conditioned on “experienced” and “young” professional personas confirms that experiential priors generate significant forecast differences under a common training leakage assumption. Across all three exercises, the source of the prior mattered more than the sophistication of the model.
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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 | Malmendier, Ulrike and Nagel, Stefan (2016) Learning from Inflation Experiences | 1.000 | 8 | 4 | 100% |
| 2 | Foroni, Claudia and Marcellino, Massimiliano and Stevanović, Dalibor (2022) Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis | 0.843 | 3 | 3 | 100% |
| 3 | Giannone, Domenico and Primiceri, Giorgio (2024) The Drivers of Post-Pandemic Inflation | 0.811 | 4 | 2 | 100% |
| 4 | Ludwig, Jens and Mullainathan, Sendhil and Rambachan, Ashesh (2025) Large Language Models: An Applied Econometric Framework | 0.811 | 4 | 2 | 100% |
| 5 | Briand, Etienne and Marcellino, Massimiliano and Stevanović, Dalibor (2025) Inflation, Attention and Expectations | 0.737 | 3 | 2 | 100% |
| 6 | Lundgaard Hansen, Anne and Horton, John J. and Kazinnik, Sophia and… (2025) Simulating the Survey of Professional Forecasters | 0.737 | 3 | 2 | 100% |
| 7 | Faria-e-Castro, Miguel and Leibovici, Fernando (2024) Artificial Intelligence and Inflation Forecasts | 0.737 | 3 | 2 | 100% |
| 8 | Dendramis, Yiannis and Kapetanios, George and Marcellino, Massimiliano (2020) A Similarity-Based Approach for Macroeconomic Forecasting | 0.644 | 2 | 2 | 100% |
| 9 | Hajdini, Ina and Kurmann, André (2026) Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts | 0.644 | 2 | 2 | 100% |
| 10 | Lee, Tae-Hwy and Parsaeian, Shahnaz and Ullah, Aman (2022) Forecasting Under Structural Breaks Using Improved Weighted Estimation | 0.644 | 2 | 2 | 100% |
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