Research Associate
Center for Financial Stability, New York City, Advances in Monetary and Financial Measurement (AMFM) team
Liting Su is a Research Associate specializing in aggregation-theoretic monetary aggregation for the Center for Financial Stability's program, Advances in Monetary and Financial Measurement. She has been contributing to the construction of the Augmented Divisia Monetary Aggregates for the United States under the direction of CFS Director Dr. William A. Barnett. Her current research focuses on incorporating credit card services into the CFS Divisia monetary aggregates to produce the Augmented Divisia Monetary Aggregates.
Ms. Su completed her doctoral studies in Economics at the University of Kansas in May, 2017. In 2015, she was appointed a Fellow of the University of Kansas, a high and rare honor for a graduate student at that university. Ms. Su received a Bachelor of Science in mathematics and applied mathematics from Renmin University of China. She also was an exchange student in the Department of Economics at the University of Paris 1 Pantheon-Sorbonne.
Center for Financial Stability, New York City, Advances in Monetary and Financial Measurement (AMFM) team
University of Kansas, Department of Economics
University of Kansas, Department of Economics
University of Kansas, Department of Economics
Ph.D. in Economics
University of Kansas, Lawrence, KS
Department of Economics
University of Paris 1, Pantheon Sorbonne, Paris, France
Bachelor of Science in Mathematics and Applied Mathematics
Renmin University of China, Beijing, China
Liting Su is a Research Associate specializing in aggregation-theoretic monetary aggregation for the Center for Financial Stability's program, Advances in Monetary and Financial Measurement. She has been contributing to the construction of the Augmented Divisia Monetary Aggregates for the United States under the direction of CFS Director Dr. William A. Barnett. Her current research focuses on incorporating credit card services into the CFS Divisia monetary aggregates to produce the Augmented Divisia Monetary Aggregates.
Ms. Su completed her doctoral studies in Economics at the University of Kansas in May, 2017. In 2015, she was appointed a Fellow of the University of Kansas, a high and rare honor for a graduate student at that university. Ms. Su received a Bachelor of Science in mathematics and applied mathematics from Renmin University of China. She also was an exchange student in the Department of Economics at the University of Paris 1 Pantheon-Sorbonne.
While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. Barnett, Chauvet, Leiva-Leon, and Su (2016) derived the aggregation and index number theory needed to measure the joint services of credit cards and money. They derived and applied the theory under the assumption of risk neutrality. But since credit card interest rates are high and volatile, risk aversion may not be negligible. We extend the theory by removing the assumption of risk neutrality to permit risk aversion in the decision of the representative consumer.
One of the hottest topics in monetary policy research has been the revival of the proposal for “nominal GDP targeting.” Recent research has emphasized the potential importance of the Divisia monetary aggregates in implementing that policy. We investigate bivariate time series properties of Divisia money and nominal GDP to investigate the viability of recent proposals by authors who advocate a role for a Divisia monetary aggregate in nominal GDP targeting.
There are two particularly relevant proposals: (1) the proposal by Barnett, Chauvet, and Leiva-Leon (2015) to use a Divisia monetary aggregate as an indicator in the monthly Nowcasting of nominal GDP, as needed in implementation of any nominal GDP targeting policy; and (2) the proposal by Belongia and Ireland (2015) to use a Divisia monetary aggregate as an intermediate target, with nominal GDP being the final target of policy.
We run well known diagnostic tests of bivariate time series properties of the Divisia M2 and nominal GDP stochastic processes. Those tests are for properties that are necessary, but not sufficient, for the conclusions of Belongia and Ireland (2014) and Barnett, Chauvet, and Leiva-Leon (2015). We find no time series properties that would contradict those implied by either of those two approaches.
In 2013, the Center for Financial Stability (CFS) initiated its Divisia monetary aggregates database, maintained within the CFS program called Advances in Monetary and Financial Measurement (AMFM), in accordance with Barnett (1980, 2012). The CFS is now making available Divisia monetary aggregates extended to include the transactions services of credit cards. The extended aggregates are called the augmented Divisia monetary aggregates and are available to the public in monthly releases. The new aggregates are also available to Bloomberg terminal users. The theory on which the new aggregates is based is provided in Barnett and Su (2014). In this paper, we provide detailed information on the data sources used in producing the new augmented Divisia monetary aggregates.
Modern aggregation theory and index number theory were introduced into monetary economics by Barnett (1980). The widely used Divisia monetary aggregates, provided to the public in monthly releases by the Center for Financial Stability in NY City, are based upon that paper. A key result upon which the rest of the theory depended was Barnett's derivation of the user-cost price of monetary assets. To make that critical part of Barnett's results available prior to publication in the Journal of Econometrics, Barnett (1978) repeated that important proof two years earlier in Economics Letters. The extension of that literature to risk with intertemporally non-separable preferences subsequently appeared in Barnett and Wu (2005). To make that result available prior to publication in the Annals of Finance, the paper's theory without proofs was provided a year earlier by Barnett and Wu (2004) in the Economic Bulletin. The theory was extended by Barnett and Su (2016a) to include the services of credit card transactions volumes under risk. The theory will appear in the proceedings volume of a conference to be held in Rome in June 2017. The proceedings will appear as a special issue of the journal, Macroeconomic Dynamics, in late 2019 at the earliest. We are making available the key results from that paper below, without the proofs. Prior to publication of Barnett and Su (2016a), the proofs will be available in the paper's online working paper version, Barnett and Su (2016b).
One of the hottest topics in monetary policy research has been the revival of the proposal for “nominal GDP targeting.” Recent research has emphasized the potential importance of the Divisia monetary aggregates in implementing that policy. We investigate bivariate time series properties of Divisia money and nominal GDP to investigate the viability of recent proposals by authors who advocate a role for a Divisia monetary aggregate in nominal GDP targeting.
There are two particularly relevant proposals: (1) the proposal by Barnett, Chauvet, and Leiva-Leon (2015) to use a Divisia monetary aggregate as an indicator in the monthly Nowcasting of nominal GDP, as needed in implementation of any nominal GDP targeting policy; and (2) the proposal by Belongia and Ireland (2015) to use a Divisia monetary aggregate as an intermediate target, with nominal GDP being the final target of policy.
We run well known diagnostic tests of bivariate time series properties of the Divisia M2 and nominal GDP stochastic processes. Those tests are for properties that are necessary, but not sufficient, for the conclusions of Belongia and Ireland (2014) and Barnett, Chauvet, and Leiva-Leon (2015). We find no time series properties that would contradict those implied by either of those two approaches.
A monetary-production model of financial firms is employed to investigate supply-side monetary aggregation, augmented to include credit card transaction services. Financial firms are conceived to produce monetary and credit card transaction services as outputs through financial intermediation. While credit cards provide transactions services, credit cards have never been included into measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities to assets. However, index number theory measures service flows and is based on aggregation theory, not accounting. Barnett, Chauvet, Leiva-Leon, and Su (2016) have derived and applied the relevant aggregation theory applicable to measuring the demand for the joint services of money and credit cards. But because of the existence of required reserves, there is a regulatory wedge between the demand and supply of monetary services. We derive theory needed to measure the supply of the joint services of credit cards and money. The resulting model can be used to investigate the transmission mechanism of monetary policy.
While credit cards provide transactions services, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities to assets. However, index number theory measures service flows and is based on aggregation theory, not accounting. We derive theory needed to measure the joint services of credit cards and money. We provide and evaluate two such aggregate measures having different objectives. We initially apply to NGDP nowcasting. Both aggregates are being implemented by the Center for Financial Stability, which will provide them to the public monthly, along with Bloomberg Terminals.
While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. We derive theory needed to measure the joint services of credit cards and money. Carried forward rotating balances are not included in the current period weakly separable block, since they were used for transactions services in prior periods. The theory is developed for the representative consumer, who pays interest for the services of credit cards during the period used for transactions. This interest rate is reported by the Federal Reserve as the average over all credit card accounts, including those not paying interest. Based on our derived theory, we propose an empirical measurement of the joint services of credit cards and money. These new Divisia monetary aggregates are widely relevant to macroeconomic research. We evaluate the ability of our money aggregate measures to nowcast nominal GDP. This is currently topical, given proposals for nominal GDP targeting, which require monthly measures of nominal GDP. The nowcasts are estimated using only real time information, as available for policy makers at the time predictions are made. We use a multivariate state space model that takes into account asynchronous information inflow, as proposed in Barnett, Chauvet, and Leiva-Leon (2016). The model considers real time information that arrives at different frequencies and asynchronously, in addition to mixed frequencies, missing data, and ragged edges. The results indicate that the proposed parsimonious model, containing information on real economic activity, inflation, and the new augmented Divisia monetary aggregates, produces the most accurate real time nowcasts of nominal GDP growth. In particular, we find that inclusion of the new aggregate in our nowcasting model yields substantially smaller mean squared errors than inclusion of the previous Divisia monetary aggregates.
While credit cards provide transaction services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. But economic aggregation theory and index number theory are based on microeconomic theory, not accounting, and measure service flows. We derive theory needed to measure the joint services of credit cards and money. The underlying assumption is that credit card services are not weakly separable from the services of monetary assets. Carried forward rotating balances are not included, since they were used for transactions services in prior periods. The theory is developed for the representative consumer, who pays interest for the services of credit cards during the period used for transactions. In the transmission mechanism of central bank policy, our results raise potentially fundamental questions about the traditional dichotomy between money and some forms of short term credit, such as checkable lines of credit. We do not explore those deeper issues in this paper, which focuses on measurement.
In Chinese, there is a famous saying: "教学相长", meaning "Teaching others teaches yourself" or "He who teaches, learns." I believe teaching is a process benefiting both the students and the teacher. The motivation to explain things in a clear, interesting, and engaging manner deepens my own understanding of the course materials, which is a very fulfilling experience for myself.
Below listed are the courses I have taught while a Ph.D. student at KU.
For teaching evaluations, please click here.
Independently taught a class of 12 senior and junior students, covering mathematical and statistical foundation, and fundamentals of regression analysis, using STATA for empirical exercises.
Teaching assistant leading discussion classes once per week for core courses offered to 1st year Ph.D. students.
Held 3 weekly discussion sessions for classes of 20 – 25 students.
Held 3 weekly discussion sessions for classes of 20 – 25 students.
Held 3 weekly discussion sessions for classes of 20 – 25 students.
Held 3 weekly discussion sessions for classes of 20 – 25 students.