| Abstract: | This paper examines the effects of temporal aggregation on the estimated time series properties of economic data. Theory predicts that temporal aggregation loses information about the underlying data processes. We derive low frequency, quarterly and annual, models implied by high frequency, monthly, structural vector autoregressive (SVAR) models and we find that these losses in information are substantial. It is shown that the accuracy of both the estimates and the forecasts of this class of models improve substantially when monthly data are used. Moreover, the aggregated data show more long-run persistence than the underlying disaggregated data. © 1998 John Wiley & Sons, Ltd. |
