Evaluating forecasts from factor models for Canadian GDP growth and core inflation - Calista Cheung; Frédérick Demers (Bank of Canada, 2007)
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.
Keywords: C32; E37; 330; Econometric and statistical methods; Wirtschaftsprognose; Gesamtwirtschaftliche Produktion; Inflationsrate; Wirtschaftsmodell; Kanada