Forecasting Fiscal Time Series Using Mixed Frequency Data

51 Pages Posted: 19 Jun 2013

See all articles by Stylianos Asimakopoulos

Stylianos Asimakopoulos

University of Bath

Joan Paredes

European Central Bank

Thomas Warmedinger

European Central Bank (ECB)

Date Written: May 13, 2013

Abstract

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.

Keywords: Fiscal policy, Mixed frequency data, Short-term forecasting, Aggregated vs. disaggregated forecast

JEL Classification: C22, C53, E62, H68

Suggested Citation

Asimakopoulos, Stylianos and Paredes, Joan and Warmedinger, Thomas, Forecasting Fiscal Time Series Using Mixed Frequency Data (May 13, 2013). ECB Working Paper No. 1550. Available at SSRN: https://ssrn.com/abstract=2264101

Stylianos Asimakopoulos (Contact Author)

University of Bath ( email )

Claverton Down
Bath, BA2 7AY
United Kingdom

Joan Paredes

European Central Bank ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Thomas Warmedinger

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

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