Forecasting Canadian Time Series with the New Keynesian Model

Ali Dib, Mohamed Gammoudi and Kevin Moran

Staff 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: C12 E32 E37 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2006
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 (10)

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Related works:
Journal Article: Forecasting Canadian time series with the New Keynesian model (2008)
Journal Article: Forecasting Canadian time series with the New Keynesian model (2008)
Working Paper: Forecasting Canadian Time Series With the New-Keynesian Model (2006)
Working Paper: Forecasting Canadian Time Series with the New-Keynesian Model (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:06-4

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