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

  • Calista Cheung
  • Frédérick Demers

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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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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