Updating Forecasts in Vector Autoregression Models with an Application to the Canadian Banking Industry

Forecasting firms' earnings has long been an interest of market participants and academics. Traditional forecasting studies in a multivariate time series setting do not take into account that the timing of data release for a specific time period of observation is often spread over several days or weeks. This paper focuses on the separation of announcement timing or data release and the use of econometric real-time methods, which we refer to as an updated vector autoregression (VAR) forecast, to predict data that have yet to be released. In comparison to standard time series forecasting, we show that the updated forecasts will be more accurate the higher the correlation coefficients among the standard VAR innovations are. Forecasting with the sequential release of information has not been studied in the VAR framework, and our approach to the six Canadian banks shows its value. By using the updated VAR forecast, we find that the relative efficiency gain is 33% in the one-step-ahead forecast compared to the ordinary VAR forecast, and 7% compared to professional consensus forecasts. Thought experiments suggest that if banks' order of information release were to change, forecast errors could be substantially reduced. These experiments emphasize that evaluating the release ordering is crucial in determining forecast accuracy