Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure

TL;DR

This paper investigates the 2021 U.S. inflation forecast failure, attributing it to sample composition biases and historical experience, and proposes adjustments that improve forecast accuracy.

Contribution

It identifies the role of historical sample biases and experience-based expectations in inflation forecasting errors and introduces methods to correct these biases.

Findings

Adjustments based on historical regimes improve forecast accuracy.

Older respondents' expectations align with past inflation experiences.

Experience-based priors significantly influence inflation forecasts.

Abstract

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.