Core Measures of Inflation as Predictors of Total Inflation

Abstract

Policymakers tend to focus on core inflation measures because they are thought to be better predictors of total inflation over time horizons of import to policymakers. We find little support for this assumption. While some measures of core inflation are less volatile than total inflation, core inflation is not necessarily the best predictor of total inflation. The relative forecasting performance of models using core inflation and those using only total inflation depends on the inflation measure and time horizon of the forecast. Unlike previous studies, we provide a measure of the statistical significance of the difference in forecast errors.JEL codes: E31, E37

Constructing and evaluating core inflation measures from component‐level inflation data

“…3 We find that core inflation constructed using weights based on the first principal component factor loadings of 17 components is statistically better than the other methods that use either 17 or 50 components. These results reinforce findings by Crone et al (2013) that the trimmed mean inflation rate is not the best forecaster of headline inflation and contradict those by Hendry and Hubrich (2006), who find that for the USA using sectoral (component) level data aggregated using regression weights provides a good forecast of aggregate headline inflation.…”

“…Our findings both confirm and differ from these previous studies and, in particular, reveal that finding a good forecaster of inflation is an empirical question that must continue to be studied as the structure of the economy evolves. The limited influence estimators such as the weighted median and trimmed mean, found to be poor forecasters in Crone et al (2013), seem to continue to be poor forecasters. In addition, we find that the disaggregated regression-based weights suggested by Hendry and Hubrich (2006) perform poorly.…”

“…However, the six benchmark methods do not include the limited influence measures (such as the trimmed mean) that we examine here and that are widely followed by economic forecasters and policymakers. Previous research has found mixed results as to whether these limited-influence estimators are good forecasters (Crone, Khettry, Mester, & Novak, 2013;Smith, 2004). In addition, the Federal Reserve Bank of Cleveland produces a monthly weighted median inflation rate of the consumer price index, and the Federal Reserve Bank of Dallas produces a monthly trimmed mean inflation rate of the personal consumption expenditure deflator, indicating that policymakers are interested in the information contained in these measures.…”

Underlying Inflation in a Dsge Model

“…Under a policy rule that reacts to it, the weights, defined as a function of the variance of the sectoral inflation, are both an input and an output of the rational expectation equilibrium. Our first contribution is offering an iterative method that allows us to compute the rational expectation solution under 5 See Rich and Steindel (2007) and Crone et al (2008) for the US, Roberts (2005) and Brischetto and Richards (2006) for Australia, Vega and Wynne (2001) for the euro area.…”

“…Examples include the Bank of Canada, Reserve Bank of Australia and European Central Bank. For a clear evidence of this statement given directly from the perspective of a policymaker seeMishkin (2007).2 This statistical property of the measures of underlying inflation has been a point of much debate in the literature:Blinder and reis (2005),Rich and Steindel (2007),Crone et al (2008), Earlier contributions includeBryan and Cecchetti (1994),Quah and Vahey (1995),Clark (2001) andCogley (2002).PUC-Rio -Certificação Digital Nº 1212325/CA…”