No abstract

Research questionConsumer price indices usually measure the change in prices for a representative basket of goods and services over time (inflation). Measurement errors can occur in a variety of ways. If the expenditure weights of individual basket items change, for example as a result of changes in consumption patterns, these adjustments are usually incorporated into the consumer price index only after a certain time lag. As a consequence, price changes are no longer depicted in a representative manner. This paper examines two sources of mismeasurement linked to the use of expenditure weights when compiling the Harmonised Index of Consumer Prices (HICP), which serves as a key metric for gauging price stability, and thus for deciding the monetary policy stance adopted in the euro area. ContributionMeasurement bias and uncertainty are quantified for the German HICP between 1997 and 2019 on the basis of publicly available price indices and consumer expenditure weights. A superlative price index capturing substitution effects more accurately than the HICP is used as a benchmark for "true" inflation. In addition to this, when computing the expenditure weights of the benchmark index, all the relevant data available at the end of the period concerned are utilised. By contrast, when calculating the HICP, it is necessary over time to rely on whatever data has most recently become available, meaning that subsequent adjustments to these often provisional data are not taken into account. Any differences between the HICP and the benchmark index are attributed to measurement errors which can be divided up into a substitution component and a data vintage component. The substitution-related divergences for the HICP of the euro area are also analysed. ResultsThe substitution component and the data vintage component generate on average an upward bias in the German HICP inflation rate of about one-ninth of a percentage point, with around 80% of all deviations falling within a range of 0 to 0.25 percentage points. The extent of mismeasurement engendered by each of these two components is broadly the same. Since 2012, when a methodological change was made to the way in which the HICP is calculated, the level of substitution-induced bias decreased slightly. However, this has been accompanied by a similarly moderate increase in data vintage-induced bias. The decline in substitution-related bias witnessed since 2012 is also evidenced by the HICP recorded for the euro area. No findings were made with respect to the impact of data vintage due to a lack of data on the euro area HICP.

This paper -which takes into consideration overall experience with the Harmonised Index of Consumer Prices (HICP) as well as the improvements made to this measure of inflation since 2003 -finds that the HICP continues to fulfil the prerequisites for the index underlying the ECB's definition of price stability. Nonetheless, there is scope for enhancing the HICP, especially by including owneroccupied housing (OOH) using the net acquisitions approach. Filling this longstanding gap is of utmost importance to increase the coverage and cross-country comparability of the HICP. In addition to integrating OOH into the HICP, further improvements would be welcome in harmonisation, especially regarding the treatment of product replacement and quality adjustment. Such measures may also help reduce the measurement bias that still exists in the HICP. Overall, a knowledge gap concerning the exact size of the measurement bias of the HICP remains, which calls for further research. More generally, the paper also finds that auxiliary inflation measures can play an important role in the ECB's economic and monetary analyses. This applies not only to analytical series including OOH, but also to measures of underlying inflation or a cost of living index.

Our paper uses micro price data collected from Germany's Consumer Price Index to compile a highly disaggregated regional price index for the 402 counties and cities of Germany. We introduce a multi-stage version of the weighted Country-Product-Dummy method. The unique quality of our price data allows us to depart from previous spatial price comparisons and to compare only exactly identical products. We find that the price levels are spatially autocorrelated and largely driven by the cost of housing. The price level in the most expensive region is about 27 percent higher than in the cheapest region.