@article{Evaluating:1407,
      recid = {1407},
      author = {Demers, Frédérick and Cheung, Calista},
      title = {Evaluating Forecasts from Factor Models for Canadian GDP  Growth and Core Inflation},
      publisher = {Bank of Canada},
      address = {2007},
      pages = {1 online resource (iii, 49 pages)},
      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.},
      url = {http://www.oar-rao.bank-banque-canada.ca/record/1407},
      doi = {https://doi.org/10.34989/swp-2007-8},
}