Money Growth Variability and Output: Evidence with Credit Card-Augmented Divisia Monetary Aggregates

2019 ◽  
Author(s):  
Jinan Liu ◽  
Apostolos Serletis
Keyword(s):  
Credit Card ◽  
Money Growth ◽  
Growth Variability ◽  
Monetary Aggregates ◽  
Divisia Monetary Aggregates

Money growth variability and output: evidence with credit card-augmented Divisia monetary aggregates

2020 ◽  
Vol 24 (5) ◽  
Author(s):  
Jinan Liu ◽  
Apostolos Serletis
Keyword(s):  
Financial Stability ◽  
Credit Card ◽  
Negative Impact ◽  
Balance Sheet ◽  
Credit Cards ◽  
Money Growth ◽  
Monetary Policies ◽  
Growth Variability ◽  
Significant Negative Impact ◽  
Growth Volatility

Abstract We reexamine the effects of the variability of money growth on output, raised by Mascaro and Meltzer (1983), in the era of the increasing use of alternative payments, such as credit cards. Using a bivariate VARMA, GARCH-in-Mean, asymmetric BEKK model, we find that the volatility of the credit card-augmented Divisia M4 monetary aggregate has a statistically significant negative impact on output from 2006:7 to 2019:3. However, there is no effect of the traditional Divisia M4 growth volatility on real economic activity. We conclude that the balance sheet targeting monetary policies after the financial crisis in 2007–2009 should pay more attention on the broad credit card-augmented Divisia M4 aggregate to address economic and financial stability.

THE CASE FOR DIVISIA MONEY TARGETING

2012 ◽  
Vol 17 (8) ◽  
pp. 1638-1658 ◽  
Author(s):  
Apostolos Serletis ◽  
Sajjadur Rahman
Keyword(s):  
Economic Activity ◽  
The United States ◽  
Money Growth ◽  
Monetary Policies ◽  
Average Growth ◽  
Monetary Aggregates ◽  
Method Of Calculation ◽  
Divisia Monetary Aggregates ◽  
Level Of Aggregation ◽  
Growth Volatility

In this paper we investigate the relationship between money growth uncertainty and the level of economic activity in the United States. We pay explicit attention to the Divisia monetary aggregates. In doing so, we use the new vintage of the data [called MSI (monetary services indices) by the St. Louis Fed], together with the simple sum monetary aggregates, over the period from 1967:1 to 2011:3. In the context of a bivariate VARMA, GARCH-in-mean, asymmetric BEKK model, we show that increased Divisia money growth volatility (irrespective of the level of aggregation and the method of calculation) is associated with a lower average growth rate of real economic activity. However, there are no effects of simple-sum money growth volatility on real economic activity, except with the Sum M1 and perhaps Sum M2M aggregates. We conclude that monetary policies that focus on the Divisia monetary aggregates and target their growth rates will contribute to higher overall economic growth.

scholarly journals Constructing Divisia Monetary Aggregates for Singapore

2021 ◽  
Vol 14 (8) ◽  
pp. 370
Author(s):  
William A. Barnett ◽  
Van H. Nguyen
Keyword(s):  
Interest Rates ◽  
Economic Activity ◽  
Credit Card ◽  
Index Number ◽  
User Cost ◽  
Monetary Aggregates ◽  
Divisia Monetary Aggregates ◽  
Index Number Theory ◽  
Future Work ◽  
Year 2000

Since Barnett derived the user cost price of money, the economic theory of monetary services aggregation has been developed and extended into a field of its own with solid foundations in microeconomic theory. Divisia monetary aggregates have repeatedly been shown to be strictly preferable to their simple sum counterparts, which have no competent foundations in microeconomic aggregation or index number theory. However, most central banks in the world, including that of Singapore, the Monetary Authority of Singapore (MAS), still report their monetary aggregates as simple summations. Recent macroeconomic research about Singapore tends to focus on exchange rates as a monetary policy target but ignores the aggregate quantity of money. Is that because quantities of money are irrelevant to economic activity? To examine the role of monetary quantities as potential monetary instruments, indicators, or targets and their relevance to predicting real economic activity in Singapore, this paper applies the user cost of money formula and the recently developed credit-card-augmented Divisia monetary aggregates formula to construct monetary services indexes for Singapore. We produce those state-of-the-art monetary services indexes from Jan 1991 to Mar 2021. We see that Divisia measures behave differently from simple sum measures in the period before the year 2000, while interest rates were high. Credit-card-augmented Divisia monetary services move closely with the conventional Divisia monetary aggregates, since the volume of credit card transactions in Singapore is relatively small compared with other monetary service assets. In future work, we plan to use our data to explore central bank policy in Singapore and to propose improvements in that policy. By making our data available to the public, we encourage others to do the same.

scholarly journals Money Neutrality, Monetary Aggregates and Machine Learning

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 137 ◽  
Author(s):  
Periklis Gogas ◽  
Theophilos Papadimitriou ◽  
Emmanouil Sofianos
Keyword(s):  
Machine Learning ◽  
Economic Activity ◽  
Central Banks ◽  
Support Vector ◽  
Short Term ◽  
Monetary Aggregates ◽  
Divisia Monetary Aggregates ◽  
Vector Machines ◽  
The World ◽  
Short Run

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.

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