Article ID: | iaor20091461 |
Country: | United Kingdom |
Volume: | 27 |
Issue: | 2 |
Start Page Number: | 95 |
End Page Number: | 108 |
Publication Date: | Mar 2008 |
Journal: | International Journal of Forecasting |
Authors: | Granger Clive W.J., Hyung Namwon |
Keywords: | ARIMA processes, Kalman filter |
This is a report on our studies of the systematical use of mixed-frequency datasets. We suggest that the use of high-frequency data in forecasting economic aggregates can increase the accuracy of forecasts. The best way of using this information is to build a single model that relates the data of all frequencies, for example, an ARMA model with missing observations. As an application of linking series generated at different frequencies, we show that the use of a monthly industrial production index improves the predictability of the quarterly GNP.