Forecasting with Many Predictors: How Useful are National and International Confidence Data?

Abstract

This paper assesses the contribution of Canadian and International (US) confidence data, drawn from consumer and business sentiment surveys, for forecasting Canadian GDP growth. The targeting approaches of Bai and Ng (2008) and Bai and Ng (2009) are employed to extract promising predictors from large databases each containing between several dozen and several hundred time series. The databases are categorised between those containing macroeconomic (Canadian and US) and confidence (Canadian and US) data, allowing us to assess the specific value added of international and confidence data. We find that forecasting ability is consistently improved by considering information from national confidence data; by contrast, their US counterparts appear to be helpful only when combined with national time-series. Overall, most relevant gains in forecasting performance are observed for short-term (up to threequarters-ahead) horizons, perhaps reflecting the timing advantage in the releases of sentiment data.