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Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation

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Author Info

  • Calista Cheung
  • Frédérick Demers

Abstract

This paper evaluates the performance of static and dynamic factor models for forecasting Canadian real output growth and core inflation on a quarterly basis. We extract the common component from a large number of macroeconomic indicators, and use the estimates to compute out-of-sample forecasts under a recursive and a rolling scheme with different window sizes. Forecasts from factor models are compared with those from AR(p) models as well as IS- and Phillips-curve models. We find that factor models can improve the forecast accuracy relative to standard benchmark models, for horizons of up to 8 quarters. Forecasts from our proposed factor models are also less prone to committing large errors, in particular when the horizon increases. We further show that the choice of the sampling-scheme has a large influence on the overall forecast accuracy, with smallest rolling-window samples generating superior results to larger samples, implying that using "limited-memory" estimators contribute to improve the quality of the forecasts.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 07-8.

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Length: 42 pages
Date of creation: 2007
Date of revision:
Handle: RePEc:bca:bocawp:07-8

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Keywords: Econometric and statistical methods;

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References

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Citations

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Cited by:
  1. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 0894, European Central Bank.
  2. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
  3. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
  4. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
  5. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
  6. Matteo Luciani & Libero Monteforte, 2013. "Uncertainty and heterogeneity in factor models forecasting," Temi di discussione (Economic working papers) 930, Bank of Italy, Economic Research and International Relations Area.

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