scispace - formally typeset
Search or ask a question
Journal Article•DOI•

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

William A. Barnett1, Jia Liu1, Ryan S. Mattson1, Jeff van den Noort•
University of Kansas1
01 Feb 2013-Open Economies Review (Springer US)-Vol. 24, Iss: 1, pp 101-124
TL;DR: 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).
Abstract: 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.
Figures (8)

Content maybe subject to copyright    Report

C E N T E R F O R F I N A N C I A L S T A B I L I T Y
D i a l o g
I n s i g h t
S o l u t i on s
1120 Avenue of the Americas, 4th Floor New York, NY 10036 T 212.626.2660 www.centerforfinancialstability.org
The New CFS Divisia Monetary Aggregates:
Design, Construction, and Data Sources
William A. Barnett, University of Kansas, Lawrence, KS
Jia Liu, University of Kansas, Lawrence, KS
Ryan S. Mattson, University of Kansas, Lawrence, KS,
and
Jeff van den Noort, Center for Financial Stability, NY City
Introduction
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 [Barnett
(1980)]. 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. Table
1 documents the component clusterings within each aggregation level, and Figure 1 displays the
resulting nesting of aggregates within aggregates, as the level of aggregation increases. 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 St. Louis Federal Reserve admirably initiated and maintains the five narrow Divisia monetary
aggregates for the US and calls them MSI (monetary services indexes), in accordance with the theory
and formulas derived by Barnett (1980). See Anderson and Jones (2011). But since the Federal Reserve
no longer provides its former broad aggregates, M3 and L, the CFS is now maintaining the broad
aggregates, Divisia M3 and Divisia M4, where M4 is similar to the Fed’s former broadest aggregate, L.
The CFS also is providing the narrow Divisia monetary aggregates, which are very similar to the St. Louis
Fed’s MSI aggregates. The primary distinction between the CFS’s and St. Louis Fed’s narrow Divisia
aggregates is the measurement of the rate of return on capital (the benchmark rate), used within the
Divisia formula. The CFS’s and the St. Louis Fed’s narrow Divisia quantity aggregates can be expected
usually to behave similarly, although their dual user-cost price aggregates behave differently. The CFS is
providing the narrow Divisia aggregates as a hedge against possible future freezes of the St. Louis
Federal Reserve’s MSI database. Such a freeze occurred for the five years of the financial crisis and
Great Recession, (March 2006-April 2011). The properly weighted broad aggregates, such as Divisia M3
C E N T E R F O R F I N A N C I A L S T A B I L I T Y
D i a l o g
I n s i g h t
S o l u t i o n s
-2-
and Divisia M4, are for most purposes the most informative. See Barnett (1982). The AMFM site focuses
primarily on the broad Divisia aggregates, which are provided solely by the CFS.
The Divisia aggregates in this CFS project are constructed for the purpose of making dependable
statistics available as painlessly as possible to the public. The most recent data are updated monthly,
will all details made public without subscription, at
http://www.centerforfinancialstability.org/amfm_data.php.
To that end and in accordance with the normal standards of science, we focus on the use of component
data available to the public. To assure replicability, we are providing all component data and are
withholding no sources or methodology or data from the public, as being proprietary to the CFS. That
task has proved to be much more complicated than anticipated. For example, national average bank
interest-rate data, previously collected and provided by the Federal Reserve, now are available only
from a private source requiring subscription fees. While much of the needed data are available within
the Federal Reserve by subscription to the private source, the Federal Reserve is currently not making
that data easily available to the public
1
. Our task has been further complicated by the discontinuance of
Federal Reserve collection of key components, such as repurchase agreements and bankers’
acceptances, previously available from the St. Louis Federal Reserve Bank website. The resources we
found for such hard-to find variables are provided in this paper, where available. Further complicating
our work is the fact that, even when component quantities are available for the former M3 and L
aggregates, those components often no longer are seasonally adjusted or consolidated. Without
seasonal adjustment, monthly growth rates contain seasonal noise; and without consolidation, simple-
sum accounting aggregation is vulnerable to distortions not consistent with reputable accounting
practices.
Data Sources
Constructing the Divisia monetary aggregates requires not only the quantities of monetary
components but also their interest rates. Following discontinuation of Federal Reserve collection of
some interest-rate and deposit-quantity data, we have had to find other public and private sources of
those data.
2
While the Federal Reserve has agreements with the firms that now collect much of the
relevant data, the Fed does not presently provide them in their Statistical Surveys or through the St.
Louis Federal Reserve Bank’s online archive tool, FRED (Federal Reserve Economic Data). Table 2
documents our current components quantity and interest-rate data sources. The following paragraphs
also document prior sources used in producing the historical series, when sources were changing.
Much quantity (volume) data are available from the FRED website. From FRED we can find
many of the aggregates’ component levels, with the exception of large-denomination time deposits,
short-term treasury bills (T-bills), and overnight repurchase agreements (repos). Large time deposits can
be found in the Federal-Reserve-Board’s H.8 release, “Survey of Assets and Liabilities at Commercial
Banks.”
3
While large time deposits were once readily available on FRED, current data from the H.8 are
no longer available there directly; one must access the data download program on the Federal-Reserve-
1
The authors would like to thank Richard G. Anderson at the St. Louis Federal Reserve Bank for providing much of
the needed data. The authors would also like to thank Steve Hanke, David Beckworth, and Peter Ireland for their
valuable input and suggestions.
2
See The Federal Reserve Discontinuation Memo on M3 and its Components.
3
http://www.federalreserve.gov/releases/h8/current/default.htm. See page 3, line 32 of the H.8 Survey for the
seasonally adjusted levels of large time deposits.
C E N T E R F O R F I N A N C I A L S T A B I L I T Y
D i a l o g
I n s i g h t
S o l u t i o n s
-3-
Board’s “Statistics and Historical Data” website. T-bills are available from the “Monthly Public
Statement of Debt” on the United States Treasury website
4
. Repos are available through the New York
Federal Reserve Bank’s “Primary Dealer Statistics” survey
5
; however, data before the beginning of this
survey had to be estimated using the now discontinued overnight and term RP level, provided by FRED
for commercial banks. Commercial paper levels are available on FRED only back to 2001; however, the
pre-2001 data can be found in the commercial paper section of the Federal Reserve Board Statistics site
6
for monthly data and table L.208 of the Z.1 release, “Flow of Funds Survey,” provided by the Federal
Reserve Board.
7
Quantity levels for the limited duration of “super negotiable orders of withdrawal”
(super NOWs) and money-market deposit accounts (MMDA) were provided in the data set used by
Anderson and Jones (2011), and in previous issues of the Federal Reserve Bulletin.
Interest-rate data are more difficult to collect, as the Federal Reserve does not make them easily
available to the public or to researchers not on their staff. The Federal Reserve itself no longer tracks
the national averages of interest rates for bank products, nor does the St. Louis Fed currently provide
them on FRED. Federal Reserve Board staff are provided these data from outside sources, with whom
the Fed has contracts: (1) Bankrate.com, a company that keeps track of interest rates on deposit
accounts, mortgage loans, and credit-card rates; (2) ICAP, a London based firm, which provides interest-
rate data for overnight and term repurchase agreements; and (3) iMoney.net, which tracks interest
rates for money-market mutual funds.
Interest rates on checking, savings, money market-accounts, and certificates of deposit of
various maturities and levels (regular or “jumbo”) are collected and provided by Bankrate.com through
two surveys: the weekly interest-rate roundup and the overnight daily internet survey. The weekly
“Bank Rate Monitor” survey is collected from the ten largest banks (five commercial and five thrift
institutions) in the 25 largest metropolitan areas of the United States. The result is a weekly sample of
250 of the largest banks by assets in the United States. The Bank Rate Monitor Survey is available for a
paid weekly subscription, which the Fed acquires and provides to its in-house researchers. The public
are not currently able to download the raw data to replicate the Fed’s research without a subscription.
The second survey is a daily “Overnight Average,” provided freely on Bankrate.com’s website, or
available using Bloomberg for commercial banks and credit unions. The Overnight Average Survey is
conducted by collecting the interest rates offered for interest-checking accounts, MMDAs, jumbo CDs of
various terms, and mortgage and credit-card offer-rates available and advertised online. The overnight
CD data follow the Bank Rate Monitor rates fairly closely; however the MMDA and interest checking
rates for commercial banks are significantly higher and more volatile (as Bankrate.com warns on its
website explanation). The discrepancy is accounted for in the survey method: Bank Rate Monitor
4
http://www.treasurydirect.gov/govt/reports/pd/mspd/mspd.htm. There is no easily downloadable time series
data for the level of Treasury bills from the MSPD. The authors can provide that series on request to simplify other
efforts to replicate the data.
5
http://www.newyorkfed.org/xml/gsds_finance.html, under the tab “Financing.”
6
http: www.federalreserve.gov/releases/cp/volumnestats.htm. See the Data Download Program for
historical survey data.
7
http://www.federalreserve.gov/releases/z1/Current/. The data are taken on a quarterly basis in the Z.1 survey.
C E N T E R F O R F I N A N C I A L S T A B I L I T Y
D i a l o g
I n s i g h t
S o l u t i o n s
-4-
surveys the same ten largest banks each week in 25 cities, while the overnight average survey compiles
rates offered from various banks online. The overnight survey often collects only 30 to 40 daily-rate
offers from different banks. These online bank offers are generally higher than the average offers to
attract customers.
As of the writing of this paper, the Federal Reserve Bank of St. Louis has agreed to begin posting
the Bankrate.com data on the FRED website in spring of 2012. The St. Louis Fed has permission from
Bank Rate Monitor to make the average interest-rate data on various account types public, and we are
indebted to Richard Anderson at the St. Louis Fed for providing those interest-rate data to us in advance
of the anticipated public availability on FRED.
Money-market funds are collected by iMoneyNet. Federal Reserve researchers have access to
the data, which again are not currently provided by the Federal Reserve Board or FRED to the public. A
subscription is needed to acquire the data from iMoneyNet. We are indebted to Richard Anderson for
providing those data to us. But the public can acquire an alternative source available for free. The “7-
Day Average Rate” on money-market funds is available to the public on Bankrate.com through their
trend graph tool (which cites the source as iMoneyNet). There is no distinction there between retail or
institutional money-market funds, so one rate is used for both, when a separate institutional rate is not
available to the public. The separate retail and institutional money-market fund rates provided to us by
the St. Louis Fed were used until October of 2011. Starting in November of 2011, the alternative 7-Day
Average Rate available on Bankrate.com is used for both retail and institutional interest rates to permit
replication with data available to the public.
Repo rate data before 1997 had to be estimated by regression on T-Bill rates. After 1997 the
interest rate data came from the London firm ICAP, which takes an average of repo rates twice daily for
their I-Repo index, found on Bloomberg and in the Wall Street Journal.
Divisia M1 Aggregate
The Divisia M1 aggregate contains the most liquid monetary-asset components. Seasonally
adjusted levels of currency, travelers’ checks, and non-interest-bearing deposits are added and then
paired with a zero interest rate. Currency is the measure of cash available within the US economy
outside of the Federal Reserve. Currency quantities are available in the Fed’s H.6 Survey and on FRED.
Travelers’ checks are freely available on FRED, as well. Like currency, traveler’s checks are assumed to
have a zero own rate or return.
While demand deposits can earn an implicit rate of return, we do not impute an implicit rate of
return to demand deposits. Anderson and Jones (2011) and Anderson, Jones, and Nesmith (1997)
investigated the possibility of assigning a non-zero own rate to demand deposits and proposed
alternative methods, originally suggested by Barnett and Spindt (1982), Farr and Johnson (1985), and
Thornton and Yue (1992). In these imputation procedures, household and business demand-deposits
are separated. As the relevant separated data are not readily available, the own rate of return for
demand deposits in the St. Louis Fed’s Divisia monetary aggregates is set to zero. The St. Louis Fed calls
C E N T E R F O R F I N A N C I A L S T A B I L I T Y
D i a l o g
I n s i g h t
S o l u t i o n s
-5-
its Divisia monetary aggregates “monetary services indexes” (MSI). While acknowledging that banks can
and do offer an implicit rate of return, especially to large depositors, we follow the same procedure as
the St. Louis Fed in foregoing the imputation of an implicit rate of return to demand deposits. The
reason is the lack of easily and openly-accessible data for such an imputation.
The levels of interest-bearing checking accounts (labeled as “other checkable deposits” or OCDs)
are also included in the aggregate. The interest rates on those accounts are available from either the
Bank Rate Monitor Survey or the Overnight Survey provided by Bankrate.com. Acquiring the own rate
of return for other checkable deposits before 1987 (the beginning of the Bank Rate Monitor survey) is
complex. From 1967 to 1973, OCDs are assumed to have a zero checking account yield, because of the
lack of data. From the period of 1974 to 1980, the own rates were the maximum allowed, which was
5%. The St. Louis Fed’s MSI index sets the own rate to be the minimum of either the 5% regulated limit
or the average of the most common interest rates reported on savings deposits in archived issues of the
Federal Reserve Bulletin. We simplify this period by adopting the 5% regulated limit for 1974 to 1980,
and the 5.25% maximum for the period from 1982 to 1983. Thrift-institution interest-checking accounts
are assumed to yield the same legal limits. In 1983, super NOW accounts were introduced, and the
super NOW interest rates are used for both other-checkable-deposits (OCDs) and the super NOW
accounts. The super NOW rates are available from the Federal Reserve Board in the Special
Supplementary Table of the Federal Reserve Bulletin back issues, accessible in the Federal Reserve
Archival System for Economic Research (FRASER) between 1983 and 1985. After January 1986, OCDs
include super NOW accounts. For the year of 1986, we use the average rate paid on NOW accounts as
provided by the Federal Reserve Board. We acquired that interest rate from the MSI component
spreadsheet provided to us by Richard Anderson for the paper Anderson and Jones (2011). After 1987
the rate or return on interest-bearing checking accounts is from the Bank Rate Monitor Survey.
Demand deposits and other-checkable-deposits (OCDs) are adjusted for retail sweeps. This
important adjustment procedure is described in the section below on “Sweeps”. Like the simple-sum
and MSI aggregates, the Divisia M1 monetary aggregate would be underestimated, if unadjusted post-
sweeps data were used for demand deposits and OCDs.
As is clear from the procedures described above, a clear and consistent data set on bank-
account interest rates is needed and would be useful, not only for construction of Divisia monetary
aggregates, which are far superior to the official simple-sum aggregates, but also for other research on
banking, finance, and monetary economics. We look forward to the anticipated FRED data availability,
planned to begin in the spring of 2012.
Divisia M2 Aggregate
The Divisia M2 aggregates include those components in the Divisia M1 aggregate, as well as
savings deposits, money-market deposit accounts, small-denomination time deposits, and retail money
funds. All of these component quantity levels come from the St. Louis Federal Reserve’s FRED database,
and the interest rates come from BankRate.com, with the exception of the national average of interest
rates for retail and institutional money market funds. The BankRate.com data are provided to us by
Citations
More filters
Journal Article•DOI•
Are there asymmetries in fiscal policy shocks

[...]

Periklis Gogas1, Ioannis Pragidis1•
Democritus University of Thrace1
17 Apr 2015-Journal of Economic Studies
TL;DR: In this paper, 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 are investigated.
Abstract: 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 ...

75 citations

Journal Article•DOI•
Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions

[...]

Apostolos Serletis1, Periklis Gogas2•
University of Calgary1, Democritus University of Thrace2
01 Feb 2014-Journal of Money, Credit and Banking
TL;DR: The authors revisited the cointegration tests in the spirit of King et al. and showed that previous rejections of the balanced growth hypothesis and classical money demand functions can be attributed to mismasurement of the monetary aggregates.
Abstract: King et al. (1991) evaluate the empirical relevance of a class of real business cycle models with permanent productivity shocks by analyzing the stochastic trend properties of postwar U.S. macroeconomic data. They find a common stochastic trend in a three-variable system that includes output, consumption, and investment, but the explanatory power of the common trend drops significantly when they add money balances and the nominal interest rate. In this paper, we revisit the cointegration tests in the spirit of King et al., using improved monetary aggregates whose construction has been stimulated by the Barnett critique. We show that previous rejections of the balanced growth hypothesis and classical money demand functions can be attributed to mismeasurement of the monetary aggregates.

72 citations

Journal Article•DOI•
Interest Rates and Money in the Measurement of Monetary Policy

[...]

Michael T. Belongia1, Peter N. Ireland2•
University of Mississippi1, Boston College2
16 Apr 2015-Journal of Business & Economic Statistics
TL;DR: This paper showed that superlative measures of money often help in forecasting movements in key macroeconomic variables, and that the statistical fit of a structural vector autoregression deteriorates significantly if such measures are excluded when identifying monetary...
Abstract: Over the last 25 years, a set of influential studies has placed interest rates at the heart of analyses that interpret and evaluate monetary policies. In light of this work, the Federal Reserve's recent policy of “quantitative easing,” with its goal of affecting the supply of liquid assets, appears to be a radical break from standard practice. Alternatively, one could posit that the monetary aggregates, when measured properly, never lost their ability to explain aggregate fluctuations and, for this reason, represent an important omission from standard models and policy discussions. In this context, the new policy initiatives can be characterized simply as conventional attempts to increase money growth. This view is supported by evidence that superlative (Divisia) measures of money often help in forecasting movements in key macroeconomic variables. Moreover, the statistical fit of a structural vector autoregression deteriorates significantly if such measures of money are excluded when identifying monetary ...

71 citations

Journal Article•DOI•
A "Working" Solution to the Question of Nominal GDP Targeting ∗

[...]

Michael T. Belongia1, Peter N. Ireland2•
University of Mississippi1, Boston College2
01 Apr 2015-Macroeconomic Dynamics
TL;DR: In this paper, a series of theoretical and empirical results are presented to argue that Divisia monetary aggregates can be controlled by the Federal Reserve and that the trend velocities of these aggregates exhibit the stability required to make long-run targeting feasible.
Abstract: Although a number of economists have tried to revive the idea of nominal GDP targeting since the financial crisis of 2008, very little has been said about how this objective might be achieved in practice. This paper adopts and extends a strategy first outlined by Holbrook Working (1923) and later employed by Hallman, et al. (1991) in the P-Star model. It presents a series of theoretical and empirical results to argue that Divisia monetary aggregates can be controlled by the Federal Reserve and that the trend velocities of these aggregates exhibit the stability required to make long-run targeting feasible.

57 citations

Journal Article•DOI•
Redundancy or Mismeasurement? A Reappraisal of Money

[...]

Joshua R. Hendrickson1•
University of Mississippi1
01 Oct 2014-Macroeconomic Dynamics
TL;DR: The authors re-examines previous empirical evidence on money demand and the role of money as an information variable using monetary services indexes as monetary aggregates, which are not consistent with economic, aggregation, or index number theory.
Abstract: The emerging consensus in monetary policy and business cycle analysis is that money aggregates are not useful as an intermediate target for monetary policy or as an information variable. The uselessness of money as an intermediate target is driven by empirical research that suggests that money demand is unstable. In addition, the informational quality of money has been called into question by empirical research that fails to identify a relationship between money growth and inflation, nominal income growth, and the output gap. Nevertheless, this research is potentially flawed by the use of simple sum money aggregates, which are not consistent with economic, aggregation, or index number theory. This paper therefore re-examines previous empirical evidence on money demand and the role of money as an information variable using monetary services indexes as monetary aggregates. These aggregates have the advantage of being derived from microtheoretic foundations as well as being consistent with aggregation and index number theory. The results of the re-evaluation suggest that previous empirical work might be driven by mismeasurement.

54 citations

References
More filters
Journal Article•DOI•
Economic monetary aggregates an application of index number and aggregation theory

[...]

William A. Barnett1•
Federal Reserve System1
01 Sep 1980-Journal of Econometrics

610 citations

Book Chapter•DOI•
The Optimal Level of Monetary Aggregation

[...]

William A. Barnett
01 Nov 1982-Journal of Money, Credit and Banking
TL;DR: In this paper, the authors describe the aggregation theoretic procedure for selecting the optimal monetary aggregate, survey the available empirical evidence for information on the optimal aggregate, and describe the procedures that should be followed to complete a redefinition of the monetary aggregates.
Abstract: IN THIS PAPER, I have three objectives: (1) to describe the aggregation theoretic procedure for selecting the optimal monetary aggregateS (2) to survey the available empirical evidence for information on the optimal aggregateS and (3) to describe the procedures that should be followed to complete a redefinition of the monetary aggregates. Although all of the Federal Reserve Board's current official aggregates are rejected as targets, some newer aggregates are found to be substantially preferable to the official aggregates. The board's staff recently completed the official redefinition of its monetary aggregates in a manner largely unrelated to aggregation and index number theory. My own research in recent years has sought to remedy that shortcoming. Except for monetary aggregates, most of the data provided by governmental agencies are constructed in accordance with aggregation and index number theory. Since aggregation theory can be applied to monetary aggregation, 1 I constructed new monetary aggregates that are consistent with the economic theory.2 Recently the empiri-

141 citations

Book•
The Theory of Monetary Aggregation

[...]

William A. Barnett, Apostolos Serletis
01 Jul 2000
TL;DR: Barnett and Serletis as discussed by the authors introduced the idea of index number theory in the context of monetary index number theories and the price of money, and proposed an extension of Index Number Theory to Capitalized Money Stock Aggregation.
Abstract: Preface. Editors' Introduction. Part I: Monetary Index Number Theory and the Price of Money. Section 1.1: Editors' Overview of Part I (W.A. Barnett, A. Serletis). Section 1.2: Derivation of the User Cost of Monetary Services. Section 1.3: The Price of Monetary Services and Its Use in Monetary Index Number Theory. Part 2: Index Number Theory. Section 2.1: Editors' Overview of Part 2 (W.A. Barnett, A. Serletis). Section 2.2: General Index Number Theory. Section 2.3: Monetary Index Number Theory. Part 3: Extensions of Index Number Theory. Section 3.1: Editors' Overview of Part 3 (W.A. Barnett, A. Serletis). Section 3.2: Extensions to Second Moments. Section 3.3: Extensions to Risk. Section 3.3.1. Monetary Aggregation Theory Under Risk. Section 3.3.2 Monetary Index Number Theory Under Risk. Section 3.4: Extensions to Capitalized Money Stock Aggregation. Part 4: Consumer Monetary Aggregation Under Perfect Certainty. Section 4.1: Editors' Overview of Part 4 (W.A. Barnett, A. Serletis). Section 4.2: General Index Number Theory. Part 5: Demand and Supply Side Monetary Aggregation by Firms and Financial Intermediaries. Section 5.1: Editors' Overview of Part 5 (W.A. Barnett, A. Serletis). Section 5.2: Production and Supply Side. Section 5.3: Extensions to Risk. Part 6: Monetary Policy with Exact Monetary Aggregation. Section 6.1: Editors' Overview of Part 6 (W.A. Barnett, A. Serletis). Section 6.2: Monetary Policy. Section 6.3: Macroeconomic Policy. Data Appendix. Section A.1: Editors' overview of appendix (W.A. Barnett, A. Serletis). Section A.2: St. Louis Federal Reserve Bank data. Appendix A. Consolidated references. Index by name. Index by subject.

131 citations

Book•
Getting it Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy

[...]

William A. Barnett
16 Dec 2011
TL;DR: Barnett as discussed by the authors argued that the crisis and recession were caused by greed and the failure of mainstream economics, and that a major increase in public availability of best-practice data was needed.
Abstract: Blame for the recent financial crisis and subsequent recession has commonly been assigned to everyone from Wall Street firms to individual homeowners. It has been widely argued that the crisis and recession were caused by "greed" and the failure of mainstream economics. In Getting It Wrong, leading economist William Barnett argues instead that there was too little use of the relevant economics, especially from the literature on economic measurement. Barnett contends that as financial instruments became more complex, the simple-sum monetary aggregation formulas used by central banks, including the U.S. Federal Reserve, became obsolete. Instead, a major increase in public availability of best-practice data was needed. Households, firms, and governments, lacking the requisite information, incorrectly assessed systemic risk and significantly increased their leverage and risk-taking activities. Better financial data, Barnett argues, could have signaled the misperceptions and prevented the erroneous systemic-risk assessments. When extensive, best-practice information is not available from the central bank, increased regulation can constrain the adverse consequences of ill-informed decisions. Instead, there was deregulation. The result, Barnett argues, was a worst-case toxic mix: increasing complexity of financial instruments, inadequate and poor-quality data, and declining regulation. Following his accessible narrative of the deep causes of the crisis and the long history of private and public errors, Barnett provides technical appendixes, containing the mathematical analysis supporting his arguments.

102 citations

Journal Article•DOI•
Building New Monetary Services Indexes: Concepts, Data and Methods

[...]

Richard G. Anderson, Barry E. Jones, Travis D. Nesmith
01 Jan 1997-Federal Reserve Bank of St Louis Review
TL;DR: The monetary services index (MSI) project of the Federal Reserve Bank of St. Louis as mentioned in this paper provides a set of statistical index numbers based on economic aggregation and statistical index number theory.
Abstract: This is the second of two articles that describe the monetary services index (MSI) project of the Federal Reserve Bank of St. Louis. The project’s MSI database, which contains the monetary services index (MSI), its dual user cost index, and other related indexes and data, is available on the Bank’s World Wide Web server.1 To facilitate comparison with the monetary aggregates published by the Board of Governors of the Federal Reserve System, all of the indexes in the MSI database are provided for the same groupings of monetary assets as the Board’s M1, M2, M3, and L aggregates.2 Indexes are provided at monthly, quarterly, and annual frequencies. The St. Louis MSI database also contains all non-confidential data and computer programs used to construct the indexes. Unlike the Board of Governor’s monetary aggregates, the monetary services indexes and their dual user cost indexes are statistical index numbers, based on economic aggregation and statistical index number theory. The previous article in this Review, “Monetary Aggregation Theory and Statistical Index Numbers,” surveys the literature on monetary aggregation theory and the use of statistical index number theory in monetary economics. Here, we discuss the construction of the monetary services index and related indexes. In the first section, we define notation and introduce some key concepts that are used throughout the article. We emphasize the distinction between real and nominal monetary asset stocks and their user costs, and we review the concepts of the real monetary services index and its nominal dual user cost index. In the second section, we define each of the indexes in the monetary services indexes database, including the following: total expenditure on monetary assets; the nominal monetary services index; the real dual user cost index; the currency equivalent index; the simple sum index; and a set of indexes based on Theil’s (1967) stochastic approach to index number theory. We emphasize that it is important to distinguish between real and nominal monetary index numbers: The aggregation theory underlying the monetary services indexes and related indexes is developed in terms of the real stocks of monetary assets, but actual monetary asset stock data are collected in nominal terms. We conclude that it is appropriate to construct a nominal monetary services index and thereafter to produce an approximation to the real monetary services index by deflating the nominal index. In the third section, we describe the monetary asset stock data. We discuss the issue of weak separability, and we define the groupings of monetary assets for which we construct indexes. These groupings correspond to the assets contained in M1, M2, M3, and L, as well as the assets contained in M1A and MZM.3 Because the aggregates are nested—each broader aggregate contains all the components of the previous, narrower aggregate—we refer to the groupings as levels of aggregation. M1A is the narrowest level of aggregation and L the broadest. In the fourth section, we discuss the own rate of return data used in the Richard G. Anderson is an assistant vice president at the Federal Reserve Bank of St. Louis. Barry E. Jones and Travis D. Nesmith are Ph.D. candidates at Washington University in St. Louis and visiting scholars at the Federal Reserve Bank of St. Louis. Mary C. Lohmann, Kelly M. Morris, and Cindy A. Gleit provided research assistance.

82 citations

Related Papers (5)
Economic monetary aggregates an application of index number and aggregation theory

[...]

01 Sep 1980-Journal of Econometrics
William A. Barnett
The user cost of money

[...]

01 Jan 1978-Economics Letters
William A. Barnett
How better monetary statistics could have signaled the financial crisis

[...]

01 Mar 2011-Journal of Econometrics
William A. Barnett, Marcelle Chauvet
Measurement Matters: Recent Results from Monetary Economics Reexamined

[...]

01 Oct 1996-Journal of Political Economy
Michael T. Belongia
Discretion versus policy rules in practice

[...]

01 Dec 1993-Carnegie-Rochester Conference Series on Public Policy
John B. Taylor
Frequently Asked Questions (7)
Q1. What have the authors contributed in "The new cfs divisia monetary aggregates: design, construction, and data sources" ?

The CFS Divisia monetary aggregates for the United States reflect closely those of the current and former simplesum monetary aggregation provided by the Federal Reserve this paper. 

If monthly growth rates are desired, the monthly levels of the Divisia aggregates can be used to compute the month-over-month growth rate, since all of their quantity data are seasonally adjusted. 

Let DD be demand deposits, DDS be sweeps-adjusted demand deposits, OCDC be othercheckable deposits at commercial banks (interest bearing checking accounts), OCDCS be the sweepadjusted OCDC, OCDT be other checkable deposits at thrift institutions, OCDTS be the sweeps-adjusted OCDT, and CD be the level of cumulative sweeps provided by FRED. 

The St. Louis Federal Reserve admirably initiated and maintains the five narrow Divisia monetary aggregates for the US and calls them MSI (monetary services indexes), in accordance with the theory and formulas derived by Barnett (1980). 

These aggregates are meant to substitute for the now discontinued Federal-Reserve simple-sum L aggregate, but with proper aggregation-theoretic weighting of the components, as opposed to the former simple-sum aggregation, which produced a greatly distorted measure of the economy’s liquidity, by weighting all components the same as legal means of payment. 

Because of lack of data, the authors have not compensated their narrow Divisia monetary aggregates for commercial sweeps, and the authors do recommend the use of broad aggregates to offset the problem. 

But the benchmark rate normally should exceed that upper envelope, since the benchmark rate is a proxy for a shadow rate having no liquidity, being the theoretical rate of return on pure capital producing no services other than investment yield.