Title : On the Stability and Growth Pact compliance: what is predictable with machine learning?
Author(s) : Kea BARET, Theophilos PAPADIMITRIOU
Abstract : The aim of the paper is to propose simplest advanced indicators to prevent internal imbalances in European Union. The paper also highlights that new methods coming from Machine Learning field could be appropriate to forecast fiscal policy outcomes, instead of traditionnal econometrics approaches. The Stability and Growth Pact (SGP) and especially the 3% limit sets on the fiscal balance purpose to coordinate fiscal policies of the European Union member states and ensure debt sustainability. The Macroeconomic Imbalance Procedure (MIP) scoreboard introduced by the European Commission aims to verify the good conduct of public finances. We propose an analysis of the determinants of the SGP compliance by the 28 European Union members between 2006 ans 2018, through a Support Vector Machine model. More than testing if the MIP scoreboard variables really matter to forecast the risk of unsustainability, we also test a set of macroeconomic, monetary, and financial variables and apply a prior feature selection model which highlights the best predictors. We give some proofs that main primary indicators of the MIP scoreboard are not useful for SGP compliance forecast and we propose new variables to forecast the European Union supranational fiscal rule compliance.
Key-words : Fiscal Rules; Stability and Growth Pact, Forecasting, Machine learning.
JEL Classification : E61, H11, H61, H62.
