Money Neutrality, Monetary Aggregates and Machine Learning
The 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 fore...
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doaj-4f6ed2e252d645869b95f76c6bd776ea2020-11-24T21:27:36ZengMDPI AGAlgorithms1999-48932019-07-0112713710.3390/a12070137a12070137Money Neutrality, Monetary Aggregates and Machine LearningPeriklis Gogas0Theophilos Papadimitriou1Emmanouil Sofianos2Department of Economics, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Economics, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Economics, Democritus University of Thrace, 69100 Komotini, GreeceThe 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.https://www.mdpi.com/1999-4893/12/7/137Eurocoinsimple sumDivisiaSVMmachine learningforecastingmoney neutrality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Periklis Gogas Theophilos Papadimitriou Emmanouil Sofianos |
spellingShingle |
Periklis Gogas Theophilos Papadimitriou Emmanouil Sofianos Money Neutrality, Monetary Aggregates and Machine Learning Algorithms Eurocoin simple sum Divisia SVM machine learning forecasting money neutrality |
author_facet |
Periklis Gogas Theophilos Papadimitriou Emmanouil Sofianos |
author_sort |
Periklis Gogas |
title |
Money Neutrality, Monetary Aggregates and Machine Learning |
title_short |
Money Neutrality, Monetary Aggregates and Machine Learning |
title_full |
Money Neutrality, Monetary Aggregates and Machine Learning |
title_fullStr |
Money Neutrality, Monetary Aggregates and Machine Learning |
title_full_unstemmed |
Money Neutrality, Monetary Aggregates and Machine Learning |
title_sort |
money neutrality, monetary aggregates and machine learning |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2019-07-01 |
description |
The 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. |
topic |
Eurocoin simple sum Divisia SVM machine learning forecasting money neutrality |
url |
https://www.mdpi.com/1999-4893/12/7/137 |
work_keys_str_mv |
AT periklisgogas moneyneutralitymonetaryaggregatesandmachinelearning AT theophilospapadimitriou moneyneutralitymonetaryaggregatesandmachinelearning AT emmanouilsofianos moneyneutralitymonetaryaggregatesandmachinelearning |
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