Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data

22 Pages Posted: 6 Jun 2020

See all articles by Stylianos Asimakopoulos

Stylianos Asimakopoulos

University of Bath

Joan Paredes

European Central Bank

Thomas Warmedinger

European Central Bank (ECB)

Date Written: January 2020

Abstract

The sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real‐time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real‐time data, the year‐end forecast errors diminish significantly when incorporating intra‐annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.

Keywords: Fiscal policy, mixed‐frequency data, real‐time data, short‐term forecasting

Suggested Citation

Asimakopoulos, Stylianos and Paredes, Joan and Warmedinger, Thomas, Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data (January 2020). The Scandinavian Journal of Economics, Vol. 122, Issue 1, pp. 369-390, 2020, Available at SSRN: https://ssrn.com/abstract=3620268 or http://dx.doi.org/10.1111/sjoe.12338

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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