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The indicator models of core inflation for Canada / by Richard Dion.FB3-2/99-13E

When there is uncertainty about estimates of the margin of unused capacity in the economy, examining a range of inflation indicators may help in assessing the balance of risks regarding the outlook for inflation. This paper tests a wide range of observable variables for their leading-indicator properties with respect to core inflation, including: commodity prices, cost indicators, measures of capacity pressures in labour and product markets, and components of the consumer price index (CPI) itself. After a preliminary screening of indicators using Granger causality tests, estimated bivariate indicator models generate post-sample static forecasts one quarter ahead and two quarters ahead over the period 1995 (Q1). A ridge regression technique is used to optimally combine selected bivariate forecasts into multivariate forecasts. The root-mean-squared errors of both the bivariate and multivariate forecasts are compared with those of benchmark models -- a Phillips curve, an autoregressive model, and two naive models.--Abstract

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Publication information
Department/Agency
  • Bank of Canada.
TitleThe indicator models of core inflation for Canada / by Richard Dion.
Series title
  • Working paper 1192-5434 99-13
Publication typeMonograph - View Master Record
Language[English]
FormatPhysical text
Other formatsDigital text-[English]
Note(s)
  • "When there is uncertainty about estimates of the margin of unused capacity in the economy, examining a range of inflation indicators may help in assessing the balance of risks regarding the outlook for inflation. This paper tests a wide range of observable variables for their leading-indicator properties with respect to core inflation, including: commodity prices, cost indicators, measures of capacity pressures in labour and product markets, and components of the consumer price index (CPI) itself. After a preliminary screening of indicators using Granger causality tests, estimated bivariate indicator models generate post-sample static forecasts one quarter ahead and two quarters ahead over the period 1995 (Q1). A ridge regression technique is used to optimally combine selected bivariate forecasts into multivariate forecasts. The root-mean-squared errors of both the bivariate and multivariate forecasts are compared with those of benchmark models -- a Phillips curve, an autoregressive model, and two naive models."--Abstract.
  • Résumés en français
Publishing information
  • Ottawa - Ontario : Bank of Canada 1999.
BindingSoftcover
Descriptionv, 20p. : references, tables ; 28 cm.
ISBN0-662-28177-2
ISSN1192-5434
Catalogue number
  • FB3-2/99-13E
Departmental catalogue number99-13
Subject terms
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