Constructing Divisia Monetary Aggregates for Singapore

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.

INTEREST RATES, MONEY, AND ECONOMIC ACTIVITY

In this paper, we are motivated by the fact that little is known about the relative performance of broad and narrow Divisia monetary aggregates, and by recent work that tests and rejects the appropriateness of the aggregation assumptions that underlie the various monetary aggregates published by the Federal Reserve as well as a large number of monetary asset groupings suggested by earlier studies. We present a comprehensive comparison of narrow versus broad Divisia monetary aggregates within three classes of empirical models. We compute correlations between the cyclical components of Divisia monetary aggregates at different levels of aggregation and the cyclical component of industrial production. We test for Granger causality running from the Divisia aggregates to industrial production and various other measures of real economic activity. We also reestimate a structural vector autoregression based on earlier work by Leeper and Roush [(2003) Journal of Money, Credit, and Banking 35, 1217–1256] and Belongia and Ireland [(2015) Journal of Business and Economic Statistics 33, 255–269; (2016) Journal of Money, Credit and Banking 48, 1223–1266], modifying that earlier work using monthly rather than quarterly data and extending it, both using broad as well as narrower Divisia monetary aggregates and by allowing for Generalized autoregressive conditional heteroskedasticity (GARCH) behavior in the structural shocks.

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 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.