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Forecasting Canadian Time Series With the New-Keynesian Model

Listed author(s):
  • Ali Dib
  • Mohamed Gammoudi
  • Kevin Moran

This paper documents the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We estimate our variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. We compare these forecasts to those arising from simple vector autoregression (VAR) models, using econometric tests for forecasting accuracy. Our results show that the forecasting accuracy of the New Keynesian model compares favorably to that of the benchmarks, particularly as the forecasting horizon is increased. These results suggest that the model could become a useful forecasting tool for Canadian time series.

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Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 382.

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Date of creation: Dec 2006
Handle: RePEc:chb:bcchwp:382
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