Forecasting Fiscal Time Series Using Mixed Frequency Data

Given the increased importance of fiscal monitoring, this study amends the existing literature in the field of intra-annual fi scal data in two main dimensions. First, we use quarterly fi scal data to forecast a very disaggregated set of fiscal series at annual frequency. This makes the analysis useful in the typical forecasting environment of large institutions, which employ a "bottom-up" or disaggregated framework. Aside from this practical type of consideration, we fi nd that forecasts for total revenues and expenditures via their subcomponents can actually result more accurate than a direct forecast of the aggregate. Second, we employ a Mixed Data Sampling (MiDaS) approach to analyze mixed frequency fiscal data, which is a methodological novelty. It is shown that MiDaS is the best approach for the analysis of mixed frequency fi scal data compared to two alternative approaches. The results regarding the information content of quarterly fiscal data con rm previous work that such data should be taken into account as it becomes available throughout the year for improving the end-year forecast. For instance, once data for the third quarter is incorporated, the annual forecast becomes very accurate (very close to actual data). We also benchmark against the European Commission s forecast and fi nd the results fare favorably, particularly when considering that they stem from a simple uni variate framework