Contributions to Monetary Aggregation

Abstract

Macroeconomics as a science has advanced significantly in the past decades, yet the quality of data on which the macroeconomic models and central bank policies are based has not been given enough attention. My dissertation centers on improving the measurement of money and aims at providing insight into some of the most important questions in the monetary aggregation literature: for instance, what is the optimal monetary aggregate? Which monetary services should be included in the monetary aggregate? And how should they be aggregated?In Chapter 1, we derive the user cost of monetary assets and credit card transaction services under the assumption of intertemporal nonseparability. Barnett and Su (2016) derives the theory permitting inclusion of credit card transaction services into Divisia monetary aggregates. The risk adjustment in their theory is based on the consumption capital asset pricing model (CCAPM) under intertemporal separability. The equity premium puzzle focuses on downward bias in the CCAPM risk adjustment to common stock returns. Despite the high risk of credit card interest rates, the risk adjustment under the CCAPM assumption of intertemporal separability might nevertheless be similarly small. While the known downward bias of CCAPM risk adjustments are of little concern with Divisia monetary aggregates containing only low risk monetary assets, that downward bias cannot be ignored once high risk credit card services are included. We believe that relaxing the intertemporal separability assumption of the CCAPM to intertemporal nonseparability could provide a nonnegligible risk adjustment, as has been emphasized by Barnett and Wu (2005). In this paper, we extend the credit card-augmented Divisia monetary quantity aggregates to the case of risk aversion and intertemporal nonseparability in consumption. Our results are for the “representative consumer" aggregated over all consumers. While credit card interest rate risk may be low for some consumers, the volatility of credit card interest rates for the representative consumer is high, as reflected by the high volatility of the Federal Reserve’s data on credit card interest rates aggregated over consumers. One method of introducing intertemporal nonseparability is to assume habit formation in consumer's preference. We explore that possibility.The main objective of Chapter 2 is to examine the information content of the credit card-augmented Divisia monetary aggregates and credit card-augmented Divisia inside monetary aggregates, recently produced by the Center for Financial Stability. We compare the inference ability of the credit card-augmented Divisia monetary aggregates and credit card-augmented Divisia inside monetary aggregates to the conventional Divisia monetary aggregates at all levels of monetary aggregation. Using cyclical correlations analysis and Granger causality tests, we find that both the conventional Divisia monetary aggregates and the credit card-augmented Divisia monetary aggregates are informative in predicting output. Moreover, during and in the aftermath of the 2007–2009 financial crisis, the credit card-augmented Divisia measures of money are more informative when predicting real economic activity than the conventional Divisia monetary aggregates. We also find that broad Divisia monetary aggregates provide better measures of the flow of monetary services generated in the economy.In Chapter 3, 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 to the broad credit card-augmented Divisia M4 aggregate to address economic and financial stability.In Chapter 4, we use nonparametric and parametric demand analysis to empirically estimate a credit card-augmented monetary asset demand system, based on the Minflex Laurent flexible functional form, and a sample period that includes the 2007-2009 global financial crisis and the Covid-19 pandemic. We also use multivariate copulae in an attempt to capture various patterns of dependence structures. In doing so, we relax the joint normality assumption of the errors of the demand system and estimate the model without having to delete one equation as is usually the practice. We show that the Minflex Laurent copula-based demand system produces a higher income elasticity for credit card transaction services and higher Morishima elasticities between credit card transaction services and monetary assets compared to the traditional estimation of the Minflex Laurent demand system. We also show that credit cards are substitutes for monetary assets and that there is lower tail dependence between the demand for credit card transaction services and transaction balances.