Simple Sum Monetary Aggregates Research Articles

Money’s predictive role in output: evidence from recent data

ABSTRACT We measure and assess the predictive performance of money in explaining output for the US, the UK, the Euro Area and Poland. Our approach, which uses more recent data and a larger set of areas, focuses on whether money ‘Granger causes’ output while taking into account the statistical properties of the data, which has implications for Granger causality tests. In addition to simple sum monetary aggregates, we use Divisia monetary aggregates, which are theoretically shown to be the actual measure of monetary services. Our results confirm that movements in money are still relevant in explaining movements in output, particularly when Divisia monetary aggregate is used. While the models using ‘narrow’ money, both simple sum and Divisia, provide mixed evidence of support, what appears to be robust across countries, is the predictive power of ‘broad Divisia’ in explaining output. Furthermore, our research finds no evidence that the statistical significance of money decreases as more variables are added to the model, or models with level variables produce more significant results than models with growth variables. In fact, rather than the level of money, it is the growth rate of money and the deviation of the growth rate of money from its time trend, which ‘Granger causes’ output.

Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation

Simple sum monetary aggregates are based on accounting conventions and have no aggregation theoretic foundations in economic theory. In contrast, Divisia monetary aggregates are directly derived from aggregation and index number theory. Credit card services cannot be included in simple sum monetary aggregates since accounting conventions cannot aggregate over assets and liabilities. However, microeconomic aggregation theory aggregates over service flows, not stocks, regardless of whether from assets or liabilities. As a result, it has recently been shown that Divisia monetary aggregates can be augmented to include credit card services and are available from the Center for Financial Stability in New York City. Other sources of consumer credit cannot be included in Divisia monetary aggregates for the United States since other sources of consumer credit in the United States are linked to specific groups of consumer goods and hence, violate the weak separability condition for the existence of an aggregator function. However, China produces a unique opportunity to broaden the Divisia monetary aggregates since sources of consumer credit, not limited to credit cards, are applicable to all consumption purchases and hence, do not violate the existence condition for an aggregator function. We report initial results with a broader Chinese Divisia monetary aggregate, including not only credit card services but also other broadly acceptable consumer loan services.

Divisia monetary aggregates and US GDP nowcasting

ABSTRACT This article nowcasts US quarterly real GDP growth rate with dynamic factor model (DFM) using Divisia Monetary Aggregate Index, Divisia M1, M2, M3, and exploits information from a large, unbalanced panel data. GDP nowcasting is evaluating the current quarter GDP given the available economic data up to the point when the nowcasting is conducted. GDP data is published quarterly with a substantial lag, while many monetary and financial decisions are made at a higher frequency. Therefore, nowcasting GDP has become an increasingly important task for central banks. This article uses DFM to nowcast GDP, compares the nowcasting results from DFM with the simple sum monetary aggregate M1, M2, M3, to the Model with weighted corresponding Divisia Index, then calculates the contributions of the Divisia Monetary index to US GDP nowcasting.

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.

The optimal monetary instrument and the (mis)use of causality tests

This paper investigates the optimal monetary instrument in a New-Keynesian model with multiple monetary assets. We compare a standard interest rate rule to a k-percent rule for three alternative monetary aggregates determined within our model: the monetary base, the simple sum measure of money, and the Divisia measure. Welfare results are striking. While the interest rate dominates the other two monetary aggregate k-percent rules, the Divisia k-percent rule outperforms the interest rate rule. Next we study the ability of Granger Causality tests – in the context of data generated from our model – to correctly identify welfare improving instruments. We find the interest rate Granger Causes both output and prices at extremely high significance levels. The same result is obtained for monetary base and the simple-sum monetary aggregate. The test results for Divisia are the weakest as Divisia fails to Granger Cause prices. We conclude that if the choice of instrument is based solely on its propensity to Granger Cause macroeconomic targets, a central bank may choose an inferior policy instrument.

Divisia and Simple Sum Monetary Aggregates: Any Empirical Relevance for Turkey?

Abstract In consideration of channels through which monetary policy affects economic activity, the monetary aggregates have been mostly ignored by the monetary authorities instead of which shortrun interest rates have been given a priori role. These monetary aggregates are largely argued to fail in measuring the effectiveness of different monetary policy regimes in forecasting the macroeconomic fundamentals. Grounded on the “Barnett critique”, the formation of traditional simple-sum monetary aggregates assuming for perfect substitution among the components of the money supply is blamed for such a failure of money in explaining the real activity. Given increasing varieties of financial assets which have completely different “moneyness”, it is important to provide an alternative measure of the money supply. Hereby, the Divisia monetary aggregates which give different weights to different assets have arisen as an alternative approach. In this study, a Divisia index is constructed to test its predictive power on quantities and prices compared to its simple sum counterpart. Accordingly, a Divisia index is built-up for Turkish economy for the period 2006-2016 to see whether the utilization of the Divisia monetary aggregates in the conduct of monetary policy makes any difference compared to that of traditional simple sum money supply. Under different specifications, though the relative power of the Divisia aggregates in predicting quantity and price variables is found, still, it can be argued that theoretically well-rounded formation of the Divisia index is not that much empirically justified for the case of Turkey.

An Interview with William A. Barnett

An Interview with William A. Barnett

Monetary Search, Proportional Transaction Costs, and the Currency Equivalent Index

Determining the correct monetary aggregate to use is (or at least should be) an important consideration when taking any monetary model to the data. In this paper I present a basic monetary search model with two assets, currency and bonds. I assume that in order for assets to be used in trade, they require some verification that the asset is authentic. Some fraction of the assets are destroyed in the verification process. This suggests that when both assets circulate in equilibrium, the appropriate monetary aggregate is a linearly homogeneous function with the weights of each asset determined by its degree of liquidity. When one normalizes the degree of liquidity of currency to 1, this implies that the appropriate monetary aggregate is the Currency Equivalent Index proposed by Rotemberg, Driscoll, and Poterba (1995). One advantage of this result is that researchers can construct a monetary aggregate that is exactly applicable to the model. Finally, I show that it is only permissible to use simple sum monetary aggregates when all assets offer the same liquidity properties or the central bank conducts policy according to the Friedman Rule.

Projections of Inflation Dynamics for Pakistan: GMDH Approach

Abstract. This study is focused on identifying, based on various forecast accuracy criteria, best inflation forecasting model for Pakistan using the in sample projections for Pakistan inflation from 2006II to 2009II. To resolve the important issue of degree of contribution in forecasting performance of the two monetary aggregates in forecasting inflation, three main predictors: real GDP, interest rate and one out of the two monetary aggregate have been used, thus constructing two models; one with Divisia Monetary Index (DMI) and other with Simple sum monetary aggregate (SSMA). It is revealed that, though both of the monetary aggregates are important predictors in forecasting inflation, but DMAs provide better fit and improved forecasts as compared to their simple sum counterpart. Hence, the evidence is established that monetary aggregates still play a dominant role in predicting inflation for Pakistan economy. The study recommends the construction, publication, and use of high frequency DMAs by the State Bank of Pakistan (SBP) for forecasting inflation in Pakistan instead of SSMAs. Finally, to identify the improvement in forecast accuracy w.r.t. different forecasts combination, these forecasts have been combined and compared. It is revealed that when the structure of an empirically observed underlying series has complex nonlinear structure then forecasts based on single nonlinear model may fail to capture these diverse complexities. The best strategy is then to use various nonlinear models and combine these forecasts. Further the study concluded that if the complex nonlinear structure of an observed series is, a priory, unknown then universal approximators like Group Method of Data Handling (GMDH)- Polynomial Neural Networks (PNNs) and GMDH-Combinatorially Optimized (CO) could provide outstandingly accurate forecasts yet avoiding ‘overfitting’ even for small sample size. Specifically, it recommends the use of nonlinear non-parametric universal approximators for forecasting inflation in Pakistan by the SBP. Keywords. Monetary aggregate, Nonparametric nonlinear models, Universal approximators, Forecasting performance, Forecasts combination. JEL. E31, E47, E51, E52.

Are there asymmetries in fiscal policy shocks?

Purpose– The purpose of this paper is to test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation.Design/methodology/approach– The authors use two alternative vector autoregressive systems in order to construct the fiscal policy shocks: one with the simple sum monetary aggregate MZM and one with the alternative CFS Divisia MZM aggregate. From each one of these systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight different types of unanticipated fiscal shocks were used next to empirically examine their effects on the growth rate and cyclical component of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock.Findings– The authors come up with three key findings: first, all fiscal multipliers are below unity but with signs as predicted by Keynesian theory. Second, government expenditures have a larger impact as compared to the tax policy and finally, positive government spending shocks are more significant than negative spending shocks. All these results are in line with previous studies and are robust through many tests using structural identification proposed by Blanchard and Perotti (2002).Practical implications– The empirical findings in this manuscript can be used for conducting a more efficient fiscal policy. The importance of government spending shocks is empirically verified along with the asymmetries related to price stickiness predicted by Keynesian theory. According to the results an efficient fiscal policy would: in terms of an expansionary policy, use government spending as a means to stimulate the economy instead of tax cuts and in the case of a contractionary policy use government revenue (higher taxes) so that the costs of this policy in terms of output lost are lower.Originality/value– In this study the authors introduce three main innovations: first, to the best of our knowledge the Divisia monetary aggregates have not yet been used to previous research pertaining to fiscal policy. Second, following Cover’s (1992) procedure of identifying monetary policy shocks we extract the unanticipated fiscal policy shocks on government spending and revenue. Finally, the authors explicitly test for the asymmetric effects on the growth rate and the cyclical component of real private GNP of a contractionary and expansionary fiscal policy.

Fiscal shocks and asymmetric effects: A comparative analysis

We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4. In doing so, we use both a vector autoregressive and the novel support vector machines systems in order to extract the fiscal policy shocks series. The latter has never been used before in a similar macroeconomic setting. Within our research framework, in order to test the robustness of our results to alternative aggregate money supply definitions we use two alternative moentary aggregates. These are the commonly reported by central banks and policy makers simple sum monetary aggregates at the MZM level of aggregation and the alternative CFS Divisia MZM aggregate. From each of these four systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight different types of unanticipated fiscal policy shocks are next used to empirically examine their effects on the growth rate and the cyclical component of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock. (This abstract was borrowed from another version of this item.)

Fiscal Shocks and Asymmetric Effects: A Comparative Analysis

We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observations over the period 1967:1 to 2011:4. In doing so, we use both a vector autoregressive and the novel support vector machines systems in order to extract the fiscal policy shocks series. The latter has never been used before in a similar macroeconomic setting. Within our research framework, in order to test the robustness of our results to alternative aggregate money supply definitions we use two alternative moentary aggregates. These are the commonly reported by central banks and policy makers simple sum monetary aggregates at the MZM level of aggregation and the alternative CFS Divisia MZM aggregate. From each of these four systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight different types of unanticipated fiscal policy shocks are next used to empirically examine their effects on the growth rate and the cyclical component of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock.

The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources

The Center for Financial Stability (CFS) has initiated a new Divisia monetary aggregates database, maintained within the CFS program called Advances in Monetary and Financial Measurement (AMFM). The Director of the program is William A. Barnett, who is the originator of Divisia monetary aggregation and more broadly of the associated field of aggregation-theoretic monetary aggregation. The international section of the AMFM web site is a centralized source for Divisia monetary aggregates data and research for over 40 countries throughout the world. The components of the CFS Divisia monetary aggregates for the United States reflect closely those of the current and former simple-sum monetary aggregates provided by the Federal Reserve. The first five levels, M1, M2, M2M, MZM, and ALL, are composed of currency, deposit accounts, and money market accounts. The liquid asset extensions to M3, M4-, and M4 resemble in spirit the now discontinued M3 and L aggregates, including repurchase agreements, large denomination time deposits, commercial paper, and Treasury bills. When the Federal Reserve discontinued publishing M3 and L, the Fed stopped providing the consolidated, seasonally adjusted components. Also the Fed no longer provides the interest rates on the components. With so much of the needed component quantity and interest-rate data no longer available from the Federal Reserve, decisions about data sources needed in construction of the CFS aggregates have been far from easy and sometimes required regression interpolation. This paper documents the decisions of the CFS regarding United States data sources at the present time, with particular emphasis on Divisia M3 and M4.

THE CASE FOR DIVISIA MONEY TARGETING

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.

FORECAST DESIGN IN MONETARY CAPITAL STOCK MEASUREMENT

We design a procedure for measuring the United States capital stock of money implied by the Divisia monetary aggregate service flow, in a manner consistent with the present-value model of economic capital stock. We permit non-martingale expectations and time varying discount rates. Based on Barnett's (1991) definition of the economic stock of money, we compute the US economic stock of money by discounting to present value the flow of expected expenditure on the services of monetary assets, where expenditure on monetary services is evaluated at the user costs of the monetary components. As a theoretically consistent measure of money stock, our economic stock of money nests Rotemberg, Driscoll and Poterba's (1995) currency equivalent index as a special case, under the assumption of martingale expectations. To compute the economic stock of money without imposing martingale expectations, we define a procedure for producing the necessary forecasts based on an asymmetric vector autoregressive model and a Bayesian vector autoregressive model. In this and a companion paper (Barnett, Chae, and Keating, 2006), we find the resulting capital-stock growth-rate index to be surprisingly robust to the modeling of expectations. The primary conclusions regard robustness. It is not our intention to advocate any particular approach to modeling future expectations. We believe that further experiments with other forecasting models would confirm our robustness conclusion. Different forecasting models can produce substantial differences in forecasts into the distant future. But since the distant future is heavily discounted in our stock formula, and since alternative forecasting formulas rarely produce dramatic differences in short term forecasts, we believe that our robustness result obviates prior concerns about the dependence of theoretical monetary-capital-stock computations upon forecasts of future expected flows. Even the simple martingale forecast, which has no unknown parameters and is easily computed with current period data, produces a discounted stock measure that is adequate for most purposes. Determining an extended index, which can remove the small bias that we identify under the martingale forecast, remains a subject for our future research. At the time that Milton Friedman (1969) was at the University of Chicago, the Chicago School view on the monetary transmission mechanism was based upon the wealth called the real balance effect or Pigou (1943) effect, of open market operations. Our research identifies very large errors in the wealth effects computed from the conventional simple sum monetary aggregates and makes substantial progress in the direction of accurate measurement of monetary-policy wealth effects. Prior experience with monetary policy errors produced from monetary-aggregate-measurement errors suggests possible formidable policy relevance.

Constructing a new monetary aggregates

This study proposes a new kind of monetary aggregates that explains the more moneyness of money assets than the simple sum monetary aggregates. Our primary purpose is to shed light on PLS modeling as a structural equation modeling (SEM) technique and to deliver guidelines for the use in money and banking. This article indentifies PLS monetary aggregates that have greater predictable power when forecasting economic activities as elements of monetary index numbers. Our findings suggest policymakers to adopt a more holistic perspective by using the SEM method based on the key objectives of monetary policy.

MEASUREMENT ERROR IN MONETARY AGGREGATES: A MARKOV SWITCHING FACTOR APPROACH

This paper compares the different dynamics of the simple-sum monetary aggregates and the Divisia monetary aggregate indices over time, over the business cycle, and across high and low inflation and interest-rate phases. Although traditional comparisons of the series sometimes suggest that simple-sum and Divisia monetary aggregates share similar dynamics, there are important differences around turning points that cannot be evaluated by their average behavior. We use a factor model with a regime-switching model that separates the common movements underlying the monetary aggregate indices from idiosyncratic variations in each series. We find that the major differences between the simple-sum aggregates and Divisia indices occur around the beginnings and ends of recessions and during some high-interest-rate phases. We note the inferences' policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia [Journal of Political Economy, 104 (5) (1996), 1065–1083] has observed in this regard, “measurement matters.”

International Financial Aggregation and Index Number Theory: A Chronological Half-century Empirical Overview

This paper comprises a survey of a half century of research on international monetary aggregate data. We argue that since monetary assets began yielding interest, the simple sum monetary aggregates have had no foundations in economic theory and have sequentially produced one source of misunderstanding after another. The bad data produced by simple sum aggregation have contaminated research in monetary economics, have resulted in needless “paradoxes,” and have produced decades of misunderstandings in international monetary economics research and policy. While better data, based correctly on index number theory and aggregation theory, now exist, the official central bank data most commonly used have not improved in most parts of the world. While aggregation theoretic monetary aggregates exist for internal use at the European Central Bank, the Bank of Japan, and many other central banks throughout the world, the only central banks that currently make aggregation theoretic monetary aggregates available to the public are the Bank of England and the St. Louis Federal Reserve Bank. No other area of economics has been so seriously damaged by data unrelated to valid index number and aggregation theory. In this paper we chronologically review the past research in this area and connect the data errors with the resulting policy and inference errors. Future research on monetary aggregation and policy can most advantageously focus on extensions to exchange rate risk and its implications for multilateral aggregation over monetary asset portfolios containing assets denominated in more than one currency. The relevant theory for multilateral aggregation with exchange rate risk has been derived by Barnett (J Econom 136(2):457–482, 2007) and Barnett and Wu (Ann Finance 1:35–50, 2005).

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases - a couple of quarters before the beginning of recessions - and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the policy relevance of the inferences. Indeed, as Belongia (1996) has observed in this regard, measurement matters.

Forecasting with Monetary Aggregates: Recent Evidence for the United States

We investigate the out-of-sample forecasting performance of various monetary aggregates in four-variate models of real output growth, inflation, changes in an interest rate, and nominal money growth from 1992 to 2004 using vector autoregressive (VAR) and regime-switching (RS) VAR models. We consider both Divisia and simple sum monetary aggregates for M1, M2M, M2, and M3 as well as sweep-adjusted M1 measures. We find little evidence that either aggregation method or level of aggregation has a big impact on the forecasting performance of our model with respect to inflation and real output growth. Our results indicate that VAR models with monetary aggregates appear to produce at best marginal improvements in RMSE over VAR models that omit money growth altogether. We also find that RS-VAR models usually provide better one quarter ahead forecasts than comparable VAR models, but often did worse when forecasting inflation four quarters ahead.