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

Calista Cheung and Frederick Demers

Staff Working Papers from Bank of Canada

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.

Keywords: Econometric; and; statistical; methods (search for similar items in EconPapers)
JEL-codes: C32 E37 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2007
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:07-8

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