Neural Network Forecasting Of Canadian GDP Growth

Abstract: The objective of this paper is to improve the accuracy of financial and monetary forecasts of Canadian output growth by using leading indicator neural network models. We find that neural networks yield statistically lower forecast errors for the year-over-year growth rate of real GDP relative to linear and univariate models. However, such forecast improvements are less notable when forecasting quarterly real GDP growth. Neural networks are unable to outperform a naive no-change model. More pronounced non-linearities at the longer horizon is consistent with the possible asymmetric effects of monetary policy on the real economy.