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Forecasting Canadian Time Series with the New Keynesian Model

Ali Dib (), Mohamed Gammoudi and Kevin Moran ()

Working Papers from Bank of Canada

Abstract: The authors document the out-of-sample forecasting accuracy of the New Keynesian model for Canada. They estimate their variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. They compare these forecasts with those arising from simple vector autoregression (VAR) models, using econometric tests of forecasting accuracy. Their results show that the forecasting accuracy of the New Keynesian model compares favourably with that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model could become a useful forecasting tool for Canadian time series. The authors invoke the principle of parsimony to explain their findings.

Keywords: Business fluctuations and cycles; Economic models; Econometric and statistical methods (search for similar items in EconPapers)
JEL-codes: E32 E37 C12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
Date: 2006
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Related works:
Working Paper: Forecasting Canadian Time Series with the New-Keynesian Model (2005) Downloads
Working Paper: Forecasting Canadian Time Series With the New-Keynesian Model (2006) Downloads
Journal Article: Forecasting Canadian time series with the New Keynesian model (2008) Downloads
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