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

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.
Variant title
  • Indicator models of core inflation for Canada
Series title
  • Bank of Canada working paper 1701-9397 99-13
Publication typeMonograph - View Master Record
Language[English]
FormatDigital text
Electronic document
Other formatsPhysical 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.
  • The catalogue number (FB3-2/99-13E), ISBN (0-662-28177-2), and ISSN (1192-5434) for the print edition have been incorrectly copied in this electronic publication.
  • Résumé en français.
Publishing information
  • Ottawa - Ontario : Bank of Canada September 1999.
Description29p.references, tables
ISSN1701-9397
Catalogue number
  • FB3-2/99-13E-PDF
Subject terms
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