Working Paper: CEPR ID: DP5485
Authors: David F. Hendry; Kirstin Hubrich
Abstract: We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others.
Keywords: disaggregate information; factor models; forecast; model selection; predictability; VAR
JEL Codes: C51; C53; E31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| disaggregate variables (C29) | aggregate forecast (E10) |
| model selection (C52) | forecast accuracy (C53) |
| changing correlation structures among disaggregate components (C10) | forecast accuracy (C53) |
| disaggregate variables (C29) | predictability of aggregate variable (y_t) (C29) |