Common and Idiosyncratic Inflation

Abstract

We use a dynamic factor model to disentangle changes in prices due to economy-wide (common) shocks, from changes in prices due to idiosyncratic shocks. Using 146 disaggregated individual price series from the U.S. PCE price index, we find that most of the fluctuations in core PCE prices observed since 2010 have been idiosyncratic in nature. Moreover, we find that common core inflation responds to economic slack, while the idiosyncratic component does not. That said, even after filtering out idiosyncratic factors, the estimated Phillips curve is extremely flat post-1995. Therefore, our results suggest that the flattening of the Phillips curve is the result of macroeconomic forces.

Understanding trend inflation through the lens of the goods and services sectors

“…While the main focus of this paper is to document new stylized facts about changes in trend inflation volatility in the goods and services sectors and how these changes relate at an aggregate level, the interpretation and policy implications of our results remain more suggestive. First, because variation in goods inflation appears to be mostly transitory since the 1990s and potentially represents foreign inflation (see, e.g., Kamber & Wong, 2020;Luciani, 2020), it raises the issue of whether monetary policy should actively offset swings in goods, or more broadly foreign, inflation. This is especially true given that there are indications that goods and services inflation may respond very differently to monetary policy (see Borio et al, 2021;Coeuré, 2019) and to the general state of business cycles (Stock & Watson, 2020).…”

Advance layoff notices and aggregate job loss

“…When states have missing observations, the EM algorithm uses the common comovements of the nonmissing states to estimate the WARN factor. DFMs are common, and other recent research has used them to estimate the common comovements in disaggregated price data (Luciani, 2020) in disaggregated wage data and in a variety of inflation expectations data (Ahn & Fulton, 2020). We describe our DFM and the EM algorithm in this section and provide details in Appendix B in the supporting information.…”