The FRBNY Staff Underlying Inflation Gauge: UIG

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractMonetary policymakers and long-term investors would benefit greatly from a measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures such as core inflation measures. This paper presents the "FRBNY Staff Underlying Inflation Gauge (UIG)" for CPI and PCE.Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, real, and financial variables. It also considers the specific and time-varying persistence of individual subcomponents of an inflation series. An attractive feature of the UIG is that it can be updated on a daily basis, which allows for a close monitoring of changes in underlying inflation. This capability can be very useful when large and sudden economic fluctuations occur, as at the end of 2008. In addition, the UIG displays greater forecast accuracy than traditional measures of core inflation.

Common and Idiosyncratic Inflation

“…Other papers have used large-dimensional dynamic factor models to study disaggregated prices in the US (e.g., Boivin et al, 2009;Maćkowiak et al, 2009;De Graeve and Walentin, 2015;Conflitti and Luciani, 2019). Among these papers, two are particularly close to ours: Reis and Watson (2010), who estimate an index of equiproportional changes in disaggregated PCE price inflation, which they label "pure" inflation; and Amstad et al (2017), who estimate a measure of underlying inflation on a dataset composed primarily of CPIs, which they call an "Underlying Inflation Gauge" (UIG).…”

“…In addition to a number of technical aspects, the main difference between our analysis and the analyses of Reis and Watson (2010) and Amstad et al (2017) is in the dataset used: our dataset is the only one that preserves the structure of PCE prices while at the same time restricting its scope to only those prices that are constructed from distinct sources. Indeed, because Reis and Watson (2010) did not control for the source of each disaggregated PCE price in their dataset, they were forced to clean and correct their data for excess cross-correlation, and, as a result, failed to preserve the structure of PCE prices.…”

“…Indeed, because Reis and Watson (2010) did not control for the source of each disaggregated PCE price in their dataset, they were forced to clean and correct their data for excess cross-correlation, and, as a result, failed to preserve the structure of PCE prices. In contrast, Amstad et al (2017) compose their price-only UIG for PCE prices using CPI price indexes and just a few aggregate (or lower-level disaggregate) PCE price indexes.…”

Evaluating underlying inflation measures for Russia

“…Considering that underlying inflation is not an observable characteristic and there is a great number of approaches towards measuring it (that is, statistical and econometric techniques allow for the realisation of the basic features of underlying inflation by more than one method), a task that emerges is to test which of the underlying inflation measures is the best from the viewpoint of the criteria reflecting the underlying inflation definition. These tests are given in Amstad et al (2014), Mankikar and Paisley (2004) and Silver (2006). In practice, it may turn out that some underlying inflation measures demonstrate good properties by some criteria and bad properties by other criteria set to underlying inflation.…”

“…The Guidelines for the Single State Monetary Policy in 2015 and for 2016 and 2017, Bank of Russia, 2014. 2 The time interval between the use of a central bank instrument (in the case of the Bank of Russia, a change in the key interest rate) and inflation response. In literature, a monetary policy lag is estimated to range from six months to two years for output and from twelve months to three years for inflation, see, for example, Mohanty (2012), Gruen, et al (1997), Duguay (1994).…”