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Forecasting Canadian Time Series With the New-Keynesian Model

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Author Info
Ali Dib
Mohamed Gammoudi
Kevin Moran

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Abstract

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
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Handle: RePEc:chb:bcchwp:382

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Caraiani, Petre, 2008. "Forecasting Romanian GDP Using a Small DSGE Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(1), pages 182-192, March. [Downloadable!]
  2. Christopher Reicher, 2009. "What Can a New Keynesian Labor Matching Model Match?," Kiel Working Papers 1496, Kiel Institute for the World Economy. [Downloadable!]
  3. Ali Dib & Mohamed Gammoudi & Kevin Moran, 2006. "Forecasting Canadian Time Series With the New-Keynesian Model," Working Papers Central Bank of Chile 382, Central Bank of Chile. [Downloadable!]
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