Common and Idiosyncratic Inflation
Bibliographic Data
| ID | 21653515 |
|---|---|
| Authors | Hie Joo Ahn (0000-0002-7949-911X, Federal Reserve Board Washington DC USA), Matteo Luciani (0000-0002-6939-3047, Federal Reserve Board Washington DC USA, corresponding author) |
| Year | 2026 |
| Volume | 41 |
| Issue | 2 |
| Pages | 156-168 |
| Publication date | 2026-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.70023 |
| OpenAlex | W4415884902 |
| Language | EN |
| References cited | 23 |
We disentangle price changes due to economy‐wide shocks from those driven by idiosyncratic shocks by estimating a two‐regime dynamic factor model with dynamic loadings on a new large dataset of finely disaggregated monthly personal consumption expenditures price inflation indexes from 1959 through 2024. We find that up to the mid‐1990s and after the COVID‐19 pandemic, common shocks were the primary driver of US inflation dynamics and had long‐lasting effects. In contrast, in the intermediate period, idiosyncratic shocks were the main driver, and common shocks had short‐lived effects
Dynamic factor · Personal consumption expenditures price index · Price setting · Price shock · Relative price · COVID-19 Pandemic Impacts · Market Dynamics and Volatility · Monetary Policy and Economic Impact
Why Has U.S. Inflation Become Harder to Forecast?
Tests of Conditional Predictive Ability
Measuring the Output Responses to Fiscal Policy
Understanding trend inflation through the lens of the goods and services sectors
How to estimate a vector autoregression after March 2020
Comparing Predictive Accuracy
Measuring the Output Gap using Large Datasets
Core Inflation and Trend Inflation
| Highly cited | No |
|---|