Forecasting Canadian Time Series with the New-Keynesian Model

Abstract

This paper documents the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We repeatedly estimate our variant of the model on a series of rolling subsamples, forecasting out-of-sample one to eight quarters ahead at each step. We then compare these forecasts to those arising from simple VARs, using econometric tests of forecasting accuracy. Our results show that the forecasting accuracy of the New Keynesian model compares favourably to that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model can become a useful forecasting tool for Canadian time series. The principle of parsimony is invoked to explain our findings.