Money Neutrality, Monetary Aggregates and Machine Learning
AbstractThe issue of whether or not money affects real economic activity (money neutrality) has attracted significant empirical attention over the last five decades. If money is neutral even in the short-run, then monetary policy is ineffective and its role limited. If money matters, it will be able to forecast real economic activity. In this study, we test the traditional simple sum monetary aggregates that are commonly used by central banks all over the world and also the theoretically correct Divisia monetary aggregates proposed by the Barnett Critique (Chrystal and MacDonald, 1994; Belongia and Ireland, 2014), both in three levels of aggregation: M1, M2, and M3. We use them to directionally forecast the Eurocoin index: A monthly index that measures the growth rate of the euro area GDP. The data span from January 2001 to June 2018. The forecasting methodology we employ is support vector machines (SVM) from the area of machine learning. The empirical results show that: (a) The Divisia monetary aggregates outperform the simple sum ones and (b) both monetary aggregates can directionally forecast the Eurocoin index reaching the highest accuracy of 82.05% providing evidence against money neutrality even in the short term. View Full-Text
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Gogas, P.; Papadimitriou, T.; Sofianos, E. Money Neutrality, Monetary Aggregates and Machine Learning. Algorithms 2019, 12, 137.
Gogas P, Papadimitriou T, Sofianos E. Money Neutrality, Monetary Aggregates and Machine Learning. Algorithms. 2019; 12(7):137.Chicago/Turabian Style
Gogas, Periklis; Papadimitriou, Theophilos; Sofianos, Emmanouil. 2019. "Money Neutrality, Monetary Aggregates and Machine Learning." Algorithms 12, no. 7: 137.
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