A user point of view on core inflation measures Laurent Bilke Euro Area Macroeconomic Developments Division ECE/ILO meeting on CPI Geneve, 11 May 2006

Introduction The need to remove short-term noise Different ways to cope with this To refer to core inflation measures is one of them

Outline (1)Defining the noise with statistical criteria (2)Three possible goals assigned to CI measures (3)Assessing the properties of CI measures (4)Illustration

(1) Statistical criteria Statistical vs model based approaches How to define the noise? First possibility: noise= sector specific developments –Most volatile items removed –Idiosyncratic component identified and removed –Extreme changes removed

(1) Statistical criteria Second possibility: noise = less lasting developments We remove the items with the lowest persistence Persistence Relationship persistence – variance, variance of the overall is: > For a given item specific noise, low persistence implies low variance

(1) Statistical criteria

Two pitfalls: (1) On the danger to exclude more than the noise Trimmed mean: we can exclude some long-term evolutions High tech goods or clothing (2) On the danger to exclude once for ever The case of energy

(1) Statistical criteria Oil prices still pure noise? Less volatile More persistent

(2) Three possible goals An indicator of current and future trends in inflation More or less emphasis can be placed on one side or the other (retrospective / prospective analysis) Emphasis on very short term developments: to remove the developments that we presume will be soon reverted – descriptive approach To predict inflation developments, x months ahead – leading indicator An intermediate: to point towards the trend, attractor

(3) How to assess the properties of CI? Depends on the assigned goal Descriptive approach –Statistical criteria Same average than headline Lower variance –Communication (Wyne, 99) Computable in real time Track record Understandable Stable in time

(3) How to assess the properties of CI? Trend/attractor approach Robalo Marques et al. (2003) 3 conditions (1) Core and headline inflation have the same trend –Cointegrated with unit coefficient (2) CI is an attractor for headline inflation –CI Granger causes headline through en ECM

(3) How to assess the properties of CI? (3) Headline inflation is not an attractor for CI –CI does not Granger causes headline through an ECM + through short-term dynamics (strong exogeneity)

(3) How to assess the properties of CI? CI as a leading indicator –Out of sample forecast error

(4) An illustration Three monthly CI measures for the EA: –HICPX: HICP excluding unprocessed food and energy –Trimmed mean, 16% on each side –Dynamic factor model, Cristadoro et al. (2004) Sample period rather short: 10 years

(4) An illustration Descriptive approach

(4) An illustration Attractor approach

(4) An illustration Leading indicator approach Almost impossible to beat an autoregressive process: Poor forecasting performance of HICPX and trimmed mean A bit better for DFM In the short-term: sectoral analysis In the long-term: broad assessment

Conclusion Useful measures but mainly for descriptive purposes The one that has some information content on trend inflation is also very hard to communicate on Treatment of energy as pure noise is problematic (ECB, 2005)

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