…using a non-parametric method to construct a leading indicator
In this paper, Hoeberichts and van den Enda develop a non-parametric method to construct a leading indicator that can detect turning points in the inflation cycle. The authors use a band-pass filter to extract the cycle and the Bry-Boschan algorithm to identify the turning points. The selection of variables for the leading indicator is based on their ability to match turning points and their dynamic correlation, with weights adjusted for cross-correlation between variables.
- Data: The authors use monthly data on core inflation for the Netherlands since 1960 and for the euro area since 1996. The six groups of indicator variables cover different segments of the economy and are related to different elements of the Phillips curve. The business cycle group relates to slack or the output gap, the prices and costs group to marginal costs, price expectations to the forward-looking Phillips curve, and external conditions and commodity prices to the cost-push shock in the Phillips curve. The financial variables relate to the transmission channels of monetary policy.
- Empirical results: Hoeberichts and van den Enda find that certain variables, such as house prices and non-energy commodity prices, meet their criteria for both the Netherlands and the euro area. In addition, they show that since the pandemic the cycle of core inflation has mainly been driven by external factors and that the inflation cycle will remain below trend well into 2024.
Detecting turning points in the inflation cycle
Authors: Marco Hoeberichts, Jan Willem van den Enda
From: De Nederlandsche Bank, Vrije Universiteit Amsterdam
…using the latest advancements in high-frequency inflation measurement
In this paper, Cavallo and Zavaleta present a methodology that combines standard structural-break methods with high-frequency data (provided by PriceStats, a private firm that has collected data online from large retailers in over 20 countries for over ten years) to identify shifts in inflation trends. The research focuses on the inflation dynamics of 25 countries from January 2022 to April 2023. The authors’ approach allows for the detection of breaks within a few weeks across a large number of disaggregated sectors, offering an advantage over traditional Consumer Price Index (CPI) data for real-time analysis. Cavallo and Zavaleta apply both single and multiple-break strategies to their data, noting that allowing for multiple breaks helps identify relevant breaks in some sectors but does not significantly alter the main findings of their research. Some of their findings include:
- If more than 50% of CPI weights are experiencing less inflation (a negative trend break), it is likely that the inflation trend has passed its peak.
- In the case of the U.S. between January 2022 and April 2023, approximately 67% of US CPI weights had experienced a negative break, 14% a positive break, and 19% had still no detectable structural change in the inflation trend. The vast majority of the sectors with more inflation are concentrated in “Recreation and Culture” and “Clothing”, two categories with relatively small weights in the CPI basket. This suggests that the US was still experiencing a significant and broad slowdown in inflation trends by early 2023.
- The majority of European countries exhibited common patterns until mid-2022 when they started to diverge. As of April 2023, the UK was near the top with 60% of weights experiencing more inflation. France was nearby with 50 % of positive breaks. On the other end was Germany, with 56% of negative breaks and only 21% of positive ones. This suggests that the factors driving inflation in Europe were becoming more country-specific at this stage of the inflation crisis.
- By April 2023, there were broad-based negative structural breaks in headline inflation trends for about half of the countries in our sample, including Spain, China, Russia, and Canada.

Detecting Structural Breaks in Inflation Trends: A High-Frequency Approach
Authors: Alberto Cavallo, Gastón García Zavaleta
From: Harvard Business School, Universidad de San Andrés