Core Inflation Measures Research Articles

PCE Inflation and Core Inflation

This paper investigates the forecasting accuracy of the trimmed mean inflation rate of the Personal Consumption Expenditure (PCE) deflator. Earlier works have examined the forecasting ability of limited-influence estimators (trimmed means and the weighted median) of the Consumer Price Index but none have compared the weighted median and trimmed mean of the PCE. Also addressed is the systematic bias that appears due to the differences in the means of inflation measures over the sample. This paper supports earlier results that limited-influence estimators provide better forecasts of future inflation than does the popular measure of core inflation, PCE inflation minus food and energy; therefore, these limited-influence estimators are core inflation.

Estimation and evaluation of core inflation measures

This article estimates and evaluates different measures of core inflation for India by employing statistical and econometric approaches. We estimate Wholesale Price Index (WPI) ex-food, WPI ex-food and energy, 20% asymmetric trimmed mean, 63rd percentile and Structural Vector Autoregression (SVAR) measures of core inflation. The trimmed mean, 63rd percentile and SVAR measures are unbiased, less volatile and highly correlated to headline inflation. The predictive accuracy of the different core inflation measures used in this article is assessed. The overall result suggests that a 20% asymmetric trimmed mean and SVAR measures of core inflation can be useful for the policy purposes.

Monetary Policy and Inflation in India: A Structural VAR Analysis

This paper provides a Vector Auto-Regression (VAR) analysis of inflation and monetary policy. It identifies monetary policy shocks in a benchmark recursive structure of VARs with two alternative inflation measures, viz., headline WPI and core inflation measure. Unlike the results in case of WPI, the response of core prices declined swiftly, following a positive call money interest rate shock. Thus, the interest rate channel is stronger for core prices. It is pertinent as RBI currently operates its monetary management through interest rate channel. The study checks robustness of results of the benchmark recursive model against alternative ordering and non-recursive identification schemes. Finally, a core price targeting scenario is generated in the benchmark VAR model with non-recursive identification scheme.

On the Asymmetric U-Shaped Relationship between Inflation, Inflation Uncertainty and Relative Price Skewness in the UK

We investigate asymmetries in the relationship between the cross-sectional mean (aggregate) rate of inflation and the second and third central moments of the cross-sectional distribution of relative prices by means of a modified Calvo pricing model with regime-dependent price rigidities. We assume that price rigidity is more pronounced when price exceeds marginal cost, a situation that we identify with weak competition and market power. Calibration experiments using realistic parameterisations reveal that the inflation-standard deviation and inflation-skewness relationships exhibit pronounced U-shaped asymmetries around the historical mean rate of inflation. UK sectoral inflation data supports our results, and indicates that our inclusion of regime-switching rigidities is important in providing an accurate characterisation of the higher moments of the distribution of sectoral price changes. We conclude that monetary policy should be designed to exploit these U-shaped relationships by targeting a level of inflation proximate to the (common) turning point. Furthermore, we conclude that core inflation measures should not be used for policy purposes as they exclude much of the information contained in the higher moments of the distribution of price changes.

Core Measures of Inflation as Predictors of Total Inflation

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. The authors 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, the authors provide a measure of the statistical significance of the difference in forecast errors. Supersedes Working Paper 08-9.

Has core inflation been doing a good job in Brazil

This paper assesses the performance of the core inflation measures calculated by the Brazilian Central Bank (BCB). The evidence shows that they do not meet some key statistical criteria that a good core inflation measure should have: unbiasedness and the ability to forecast inflation. That performance stems, in part, from the lack of a well-grounded statistical and economical basis underlying them. Three new measures are built and assessed using the same criteria. The evidence shows that although their behaviour is more in accordance to what the theory claims, they still lack the ability to help forecasting inflation. Hence both the BCB and the market should use core inflation cautiously.

FOOD INFLATION IN SOUTH AFRICA: SOME IMPLICATIONS FOR ECONOMIC POLICY

This paper analyses the trends in food price movements in South Africa between 1980 and 2008. There are three main results emanating from the analysis in this paper. Firstly, food price movements have played a large role in generating inflationary episodes in South Africa. Secondly, while external influences do matter, South African food price movements are mainly due to domestic influences. This implies that national policy has an important role to play in taming domestic food price inflation. Thirdly, given the strong second round impacts, food price movements warrant special attention in monetary policymaking. Core measures of inflation that exclude food price movements may not accurately reflect the underlying inflationary pressures in the economy and could compromise the attainment of the goal of price stability.

A policy-sensible benchmark core inflation measure

Core or underlying inflation indicators feature prominently in the economic debate and in internal and external policy discussions of central banks. Notwithstanding this popularity the role of core inflation measures in the monetary policy-making process is questionable since most of these measures are based exclusively on statistical criteria without any theoretical justification. In this paper we propose an approach to building a benchmark measure of core inflation that explicitly treats core inflation as an artificial concept whose usefulness rests exclusively with its use in improving monetary policy effectiveness. Using this measure, constructed on the basis of the solution to a standard optimal monetary policy problem, we assess the performance and policy usefulness of other, popular core inflation indicators. Our approach is illustrated by means of two applications to euro-area and US data. Copyright 2011 Oxford University Press 2011 All rights reserved, Oxford University Press.

Monetary Policy and Inflation in India: A Structural VAR Analysis

This paper provides a Vector Auto-Regression (VAR) analysis of inflation and monetary policy. It identifies monetary policy shocks in a benchmark recursive structure of VARs with two alternative inflation measures, viz., headline WPI and core inflation measure. Unlike the results in case of WPI, the response of core prices declined swiftly, following a positive call money interest rate shock. Thus, the interest rate channel is stronger for core prices. It is pertinent as RBI currently operates its monetary management through interest rate channel. The study checks robustness of results of the benchmark recursive model against alternative ordering and non-recursive identification schemes. Finally, a core price targeting scenario is generated in the benchmark VAR model with non-recursive identification scheme.

Core Versus Headline Inflation: An Opportunity for Greater Transparency

T he core inflation measures—which exclude food and energy—are commonly used in monetary policy deliberations. Recently, the Federal Open Market Committee (FOMC) has been accused of being out of touch with consumers because the prices of groceries and gas have increased much more rapidly than the prices of the goods reflected in the core measures.1 In a recent speech, Janet Yellen, Vice Chair of the Fed’s Board of Governors, defended the FOMC’s use of core measures, saying that the Committee’s “focus on core and other inflation measures that may exclude recent increases in the cost of gasoline and other household essentials is not intended to downplay the importance of these items in the cost of living or to lower the bar on the definition of price stability.” Rather, she said, “The Federal Reserve aims to stabilize inflation across the entire basket of goods and services that households purchase, including energy and food,” but the FOMC focuses on core inflation measures because “in light of the volatility of food and energy prices, core inflation has been a better forecaster of overall inflation in the medium term than overall inflation itself has been over the past 25 years.”2 This essay notes that the evidence that core inflation is a better predictor of future headline inflation is mixed and presents the results of a simple test of the proposition that core inflation is a better predictor of future headline inflation than headline inflation. The essay concludes by showing that over periods of interest to consumers, the difference in the loss of purchasing power reflected by the core and headline measures is economically relevant. Not only is the evidence of the relative predictive power of the core versus the headline measures mixed, but much of the pertinent literature investigates the predictive power of core inflation measures that differ from those used by the FOMC.3 Consequently, surprisingly few studies address the hypothesis that core inflation used by the FOMC better predicts future headline inflation than headline inflation itself. In addition, much of the existing research uses a one-year forecast horizon, which is likely shorter than the “medium-term” horizon of interest to policymakers. Speci fically, I test whether the average level of core inflation or headline inflation over the most recent two or three years is a better predictor of headline inflation over the next two or three years.4 Twoand three-year forecast horizons are assumed to represent the “medium term.” I use a standard procedure to test whether the difference in the two forecasts is statistically significant in terms of either the mean square forecast error (MSFE) or the mean absolute forecast error (MAFE). Vice Chair Yellen did not specify a particular inflation index, so the analysis presented Core Versus Headline Inflation: An Opportunity for Greater Transparency

Reconsidering the Role of Food Prices in Inflation

Food prices are generally excluded from measures of inflation most closely watched by policymakers due either to their transitory nature or their higher volatility. However, in lower income countries, food price inflation is not only more volatile but also on average higher than nonfood inflation. Food inflation is also in many cases more persistent than nonfood inflation, and shocks in many countries are propagated strongly into nonfood inflation. Under these conditions, and particularly given high global commodity price inflation in recent years, a policy focus on measures of core inflation that exclude food prices can misspecify inflation, leading to higher inflationary expectations, a downward bias to forecasts of future inflation and lags in policy responses. In constructing measures of core inflation, policymakers should therefore not assume that excluding food price inflation will provide a clearer picture of underlying inflation trends than headline inflation.

Reconsidering the Role of Food Prices in Inflation

Food prices are generally excluded from measures of inflation most closely watched by policymakers due either to their transitory nature or their higher volatility. However, in lower income countries, food price inflation is not only more volatile but also on average higher than nonfood inflation. Food inflation is also in many cases more persistent than nonfood inflation, and shocks in many countries are propagated strongly into nonfood inflation. Under these conditions, and particularly given high global commodity price inflation in recent years, a policy focus on measures of core inflation that exclude food prices can misspecify inflation, leading to higher inflationary expectations, a downward bias to forecasts of future inflation and lags in policy responses. In constructing measures of core inflation, policymakers should therefore not assume that excluding food price inflation will provide a clearer picture of underlying inflation trends than headline inflation.

The Usefulness of Core PCE Inflation Measures

This paper examines a number of alternative PCE price inflation measures including overall PCE inflation, PCE inflation excluding food and energy, trimmed mean PCE inflation, component-smoothed inflation, variance-weighted inflation, inflation with weights based on disaggregated regressions, and survey measures of inflation expectations. When averaging across a handful of specifications based on the primary uses of a core inflation measure three conclusions arise: 1. Inflation rates for nearly all the measures best track ex-post trend inflation or predict future overall inflation when they are averaged over a considerable number of months. Overall PCE price inflation should be averaged over 18 months or longer. A shorter averaging period is appropriate for core measures, often on the order of 12 months. 2. Even after appropriately averaging each index, core inflation indexes generally perform better than overall inflation. 3. Exclusion indexes, such as PCE excluding food and energy, perform slightly worse than many other possible core inflation measures; trimmed mean PCE, or a variance-weighted index, may be better choice for a summary inflation measure.

The Usefulness of Core PCE Inflation Measures

This paper examines a number of alternative PCE price inflation measures including overall PCE inflation, PCE inflation excluding food and energy, trimmed mean PCE inflation, component-smoothed inflation, variance-weighted inflation, inflation with weights based on disaggregated regressions, and survey measures of inflation expectations. When averaging across a handful of specifications based on the primary uses of a core inflation measure three conclusions arise: 1. Inflation rates for nearly all the measures best track ex-post trend inflation or predict future overall inflation when they are averaged over a considerable number of months. Overall PCE price inflation should be averaged over 18 months or longer. A shorter averaging period is appropriate for core measures, often on the order of 12 months. 2. Even after appropriately averaging each index, core inflation indexes generally perform better than overall inflation. 3. Exclusion indexes, such as PCE excluding food and energy, perform slightly worse than many other possible core inflation measures trimmed mean PCE, or a variance-weighted index, may be better choice for a summary inflation measure.

Measuring core inflation in Italy comparing aggregate vs. disaggregate price data

This paper focuses on the core inflation measurement in Italy using univariate (national-level inflation) vs. multivariate (city-level inflation) models during the period 1970–2006. We derive algebraic expressions that allow comparison between the reduced form parameters of univariate and multivariate local level models in the context of contemporaneous and temporal aggregation. We illustrate the relevance of these theoretical results for the empirical analysis of time series. Using Italian data, we find that multivariate and univariate models extract similar core inflation measures when analyzing the moderate-low inflation period. In contrast, the two competing models yield different trends when modeling the Great Inflation period.

Using wavelets to measure core inflation: The case of New Zealand

This paper uses wavelets to develop a core inflation measure for inflation targeting central banks. The analysis is applied to the case of New Zealand – the country with the longest history of explicit inflation targeting. We compare the performance of our proposed measure against some popular alternatives. Our measure does well at identifying a reliable medium-term trend in inflation. It also has comparable forecasting performance to standard benchmarks.

Various Measures of Underlying Inflation in Poland 1995 - 98

As inflation targeting gains popularity policy makers, monetary authorities seek to design a measure of inflation that would be a good indicator of fundamental demand-driven price movements, i.e. the underlying or core rate of inflation. It is widely acknowledged that the Consumer Price Index (which is the simple weighted average of price changes of the set of goods and services comprising the consumers' expenditure basket) is a rather deficient indicator of the 'trend' inflation as it is highly volatile, seasonal and contains a lot of noise. The ideal measure of core inflation should account for the long-term trend movements in prices that reflect the state of demand in the economy and discard various one-off shocks coming from supply side. The paper presents 4 alternative methods of calculating the core inflation most commonly found in the literature: trimmed mean, sample mean percentile, standard deviation trimmed mean and exclusion mean. Using Polish price data from the period 1995:1-1998:7, each measure is calculated at monthly, quarterly and annual frequencies and compared to the 24-month centered moving average .of the CPI which is assumed to be the benchmark core inflation. Root mean square error (RMSE) and mean absolute deviation (MAD) of the candidate measure and the benchmark were chosen to be the criteria for choosing the optimal definition - both within each of the 4 groups and across them. Rather surprisingly, crude methods based on exclusion yielded the best results. Volatility-based exclusion proved most efficient for monthly and quarterly series, whereas excluding broad aggregates (food and energy) turned out optimal for annual series. The paper concludes with highlighting the caveats and fragility of the results as well as stressing the necessity of further research.

UCM: A measure of core inflation

The primary objective of this paper is to establish the superiority of Unobserved Components Models (UCMs) as a measure of core inflation over alternative econometric methods, namely Structural Vector Autoregressive (SVAR). UCMs have the advantage of allowing the policy makers to decide which components of the headline inflation should be defined as permanent and which one as transitory on the basis of their duration. The paper also comments on the comparative performance of annually differenced series over seasonally differenced series. The UCMs are found to generate reasonable medium to long-term out-of-sample forecasts of Wholesale Price Index (WPI) inflation.

A new approach to measuring core inflation for Turkey: SATRIM

Policymakers are interested in the general trend of inflation. Headline inflation is subject to different shocks and contains noise due to the heterogeneity inherit in it. Therefore, core inflation measures, which are better capable of indicating general trend, are developed. Atuk and Ozmen (2009) introduced and discussed various core inflation measures for Turkey. Among various measures discussed, trimmed mean inflation –TRIM– performs relatively better. However, this measure fails to distinguish signal and noise when dealing with seasonal items. To overcome this drawback, this study introduces a new core inflation measure SATRIM–seasonally adjusted trimmed mean inflation. This methodology seasonally adjusts disaggregated price series and uses seasonally adjusted monthly figures in trimming process. Overall, SATRIM outperforms TRIM measure on the basis of trend tracking ability in different time spans. Moreover, SATRIM, which is dynamic in nature, is more careful when dealing with seasonal items, focusing on non-seasonal extreme changes.

The monetary transmission in the euro area: post-1999 data assessment

This article investigates the transmission of monetary policy shocks in the euro area after 1999. To examine this we use empirical structural vector error correction models, built on the data for the period from 1999 to mid-2005. We identify a monetary shock by restricting it to have only temporary effects on output and inflation. We obtain negative responses of inflation and output to a restrictive monetary shock in the period under investigation. These results are obtained with the HICP based as well as the core inflation measure included in the model.