Download Asset Substitutes, Money Demand and the Inflation Process in Brazil

Asset Substitution, Money Demand, and the Inflation Process in Brazil
Author(s): Charles W. Calomiris and Ian Domowitz
Source: Journal of Money, Credit and Banking, Vol. 21, No. 1 (Feb., 1989), pp. 78-89
Published by: Ohio State University Press
Stable URL: http://www.jstor.org/stable/1992579
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CHARLESW. CALOMIRIS
IAN DOMOWITZ
Asset Substitution,MoneyDemand,
and the InflationProcessin Brazil
THERELATIVE
SOPHISTICATION
OFFINANCIAL
MARKETS
in Brazil
sets it apart from other developing economies. In the last twenty years, Brazilhas
provedfertileground for Elnancialinnovationsin responseto high ratesof inflation,
reserverequirements,and low or zero nominal interestrateceilingson conventional
bankaccounts. Theseinnovationsincludethe rapidgrowthof relativelyunregulated
Elnancecompanies, the emergenceof a fledglingequitiesmarket,reductionsin bank
transactingcosts through computerization,and the use of bank repurchaseagreements as substitutesfor conventional deposits (Gelb et al. 1980).
Primaryand secondary markets for government securities,which also provide
the basis for bank repurchaseagreements,emergedin the early 1970sas well. These
securitiesprovided a convenient source of funds for the government in the face of
shrinkingmoney demandas they permittedagents to maintainrelativelyhigh yields
and liquiditywithout resortto nonElnancialor foreignassets. Though the availability of treasurybills and indexed bonds contributedto the fallingdemandfor money,
the minimum denomination and transaction costs associated with holding and
trading these instrumentsensured that some agents would retain funds in zero or
low-interestaccounts. At the sametime, those who mighthavetransferredfunds out
of the domestic Elnancialsystem, and beyond the reach of the government, were
offered relativelyattractive alternatives.Thus one can view the creation of these
Thisresearchwassupportedin partby NSF grantSES 85-20097to the secondauthorandby the
Centerfor UrbanAffairsand PolicyResearch,Northwestern
University.TheauthorsthankPatrice
RobitailleandGordonPhillipsforhelpwithdata,andtwo anonymousrefereesforcommentsgreatly
improvingthe focusandexpositionof the paper.
CHARLES
W. CALOMIRIS
is assistantprofessor of economics, Northwestern University.
IANDOMOWITZ
is associate professor of economics and is associated with the Centerfor
UrbanAffairs and Policy Research, Northwestern University.
Journal of Money, Credit,and Banking, Vol. 21, No. 1 (February 1989)
Copyright i' 1989by the Ohio State University Press
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CHARLESW. CALOMIRISAND IAN DOMOWITZ : 79
securitiesas a discriminatoryfinancial taxation policy in which the most inelastic
asset demands receivethe highest taxation rates.
The peculiar features of Brazil's financial system have been recognized as a
stumbling block to estimatingthe demand for money and for isolating the role of
money in the inflation process (Gelbet al. 1980).Below we presentevidencethat the
availabilityof alternativeassetsas a sourceof financefor the governmentand a store
of value for agents has had importantquantifiableeffects on the demandfor money
and the relationships among government deficits, money growth, and inflation.
Takingaccount of alternativeassets allows one to explain much of the observedrise
in monetary velocity (see Figure 1) and the puzzling fact that Brazil, unlike other
high-deficitdevelopingeconomiesexperiencinghigh inflation,shows money growth
innovationsfollowing ratherthan precedingthose in inflation (Hanson 1980).
We first develop a steady-state model of money-market equilibrium, which
stressesthe long-term connections between governmentfinancial policy, inflation,
and real asset supplies and demands. This equilibrium model is nested within a
short-termdynamic model allowing deviations from equilibriumin section 2. In
estimatingand testing the model we pay particularattention to problems of model
specificationcommonly encounteredin the estimationof money demandequations.
Wefind that-contrary to the resultsof previousstudies Brazilianmoney demand
appearsresponsiveand stable. Section 3 turnsto the issue of the connectionbetween
government deficits and inflation. There we argue that the peculiar pattern of
intertemporal"causeand effect"between money and prices in Brazil is consistent
with the predictionsof section 1. Specifically,innovations in deficitscan be seen as
the 'Yorcingprocess"to which other nominal variablesrespond.
1. ESTIMATING MONEY DEMAND
The estimation of money demand provides a short-termindicator of economic
activityand a long-termguide to inflationtargeting.In Brazil,selectivegovernment
intervention in asset markets (e.g., interest rate ceilings, time-varying inflation
indexation rulesand treasurybill supply policy) along with high and changingrates
of inflationmake interestratesunappealingshort-termindicators.Data on GNP is
generallyconsideredpoor and is subjectto extensive revisions.From the long-term
perspective,the persistenceof high and varying rates of inflation and the government'srelianceon the inflationtax furthermotivatean interestin money demand,in
order to connect long-termgovernmentpolicy with the time path of inflation.
Money demand estimation in the Brazilian context entails a unique set of
potential advantages and pitfalls. The potential problems include an active and
expanding market for repurchaseagreements backed by treasury securities, the
computerizationof banking in the 1970s,and the existence of a black market for
dollars. The latter provides a potential alternativeto cruzeiro holdings, while the
former two imply a change in transactioncost due to improvementsin transaction
technology and the availablerangeof accessiblealternativeassets. Dornbuschet al.
(1983)find that the dollarblack marketin Brazilappearsto be unrelatedto currency
substitution.Below we reportevidence consistent with this view.
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80 : MONEY,CREDIT,AND BANKING
2.8
2.6
-
_
2.4
-
_
2.2
<
-
2-
I .8
\
'
1974
I
1975
|
1976
.
1977
.
1978
.
1979
.
1980
1981
FIG. 1. Real Money Balances Measures the Log of the Ratio of Nominal Money to the Consumer Price Index
(Sources are given in the Data Appendix.)
On the positive side, high and varying rates of inflation magnify incentives for
active short-termportfolio adjustment,allowing identificationof such adjustments
on an empiricallevel. Moreover, Brazilianasset marketsenjoyjust the rightdegree
of regulation, variety, and sophistication for the purposes of money demand
estimation;moneyholdersmay choose to hold any of severalalternativeassets with
observablerates of returnwhich vary independentlyof one another mainlybecause
of regulation.
The existing literatureon money demandin Brazilbypassessome of these unique
opportunities.Indeed, many studiesignorethe existence of interest-bearingdomestic assets in Brazil, and focus instead on the expected rate of inflation. Moreover,
existing models have not emphasizedthe role of the treasurybill marketin influencing money demand.1
Domestic assets in Brazilfor the period 1972-1981 may be classified usefully by
the determinantsof their rates of return:cash and demand deposits that earn zero
nominal interest, assets with pre-fixed interest whose principalreceivesa government-determinedrate of indexation2(retirementaccounts, governmentbonds, and
passbook savings accounts), governmenttreasurybills which also serveas backing
for repurchaseagreements,"billsof exchange"issued by finance companies which
earn a market-determinedrate of return,3and bank time deposits which earn
pre-fixed, regulatedrates of return.
tSee Calomirisand Domowitz(1987)for a literaturereview.
2Duringourperiodalmostall indexedassetsreceived"monetary
correction,"
whilea fewreceived
the moregenerous"exchangecorrection."
Theindexationformulaearerelatedto pastinilation,but
subjectto frequentanddrasticchange.Forexample,a ceilingof 50 percentwasplacedon monetary
correctionfor 1980,whenthe inflationratewasnearlytwicethat.
3 Ratesof returnon billsof exchangewerelimitedby regulation
before1975.Weusetheconsumer
creditratein RiodeJaneiroas ourmeasureof thereturnon billsof exchange.Thisallowsusto capture
nonpecuniary
"convenience"
servicesorothermeansbywhichcompetingElnanceiras
transferred
excess
proElts
whichcamefrominterestrateceilings.
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CHARLESW. CALOMIRISAND IAN DOMOWITZ : 81
0.8
0.4_
SJ
0.4-
J
A/
A
1.2 - TJ
- 1.6 N
|
l
|
1974
1975
1976
l
1977
I
|
l
I
1978
1979
1980
1981
FIG. 2. T-bill Velocity Measuresthe Log of the Ratio of Total Transactionin the Secondary Market for T-bills to the
Public's Holdings Stock of T-bills (Sources are given in the Data Appendix.)
We allow relative rates of returnas well as income and transactingcost to enter
the demand for zero-interestmoney:
(M/
pd
wU1 /12,
=
/j3
u(ils
i2, i3,
wU4 <
0
u5
7re,
y,
wU6 >
F),
(1)
Os
where Mdenotes public holdings of cash and demand deposits, Pthe price level, il
the rate of indexation ("monetarycorrection"),i2 the yield on bills of exchange, i3
the yield on T-bills, Re the expected rate of inflation, Yrealincome, and Fthe cost of
transactingin Elnancialmarkets.
Bank repurchaseagreements act as a money substitute. The growth of transactions in the secondary market for treasury bills, and the rising transactions
"velocity"of treasury bills (i.e., the ratio of secondary market transactions to
existing T-bill stock) are evidence for the increasing use of T-bill repurchase
agreements as a substitute for cash. Figure 2 shows that T-bill velocity increased
tenfold from 1972to 1981. Though repurchaseagreementsare not separablefrom
other T-bill transactions,the increasein T-bill velocity at least in part reflectsthe
increasingattractivenessof repurchaseagreements.Weemploy T-billvelocity( F) as
a proxy for the (negativeof the) cost of transactingin these instruments.Wewrite(1)
as:
(M/P)
=
wU(ils i2, i3,
1rS YS
V)
wU6 <
O.
(2)
Brazilianfinancial institutions allow some a priori identification of exogeneity
and endogeneity of variableswhich enter money demand. The rate of indexation il
is pre-Elxed,while the other variables are simultaneously determined with the
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82 : MONEY,CREDIT,AND BANKING
supplies of money and other securities. The nominal supplies of money, treasury
bills and bills of exchange, and the pricelevel are not predeterminedwith respectto
money demand but depend crucially on deficit policy and asset demands. The
budgetconstraintof the governmentrequiresthat nominaltaxes (X) net of transfers
(R) and expenditures(G) be financed by the net creation of governmentliabilities
(D), which consist of nonmonetary liabilities of the monetary authority (NM),
outside money (C), treasurybills (p, and indexed bonds (IB):
R + G-X
(3)
= 1vD = 1vC+ 1vT+ 1vIB + 1vNM.
The government divides exogenous real debt creation among treasury bills,
treasury bonds, and liabilities of the monetary authority. Often liabilities of the
monetaryauthoritycorresponddirectlyto governmentloan items or pass-throughs
(subsidiesto commercial banks for particularloans). Some direct loans and passthroughs are backed by bona fide loans, while others have been made with little
expectation of repayment. Thus it is difficult to measure the implicit transfer
accomplished through central bank programs. When the monetary authority
createsliabilitiesthrough direct loans or loan pass-throughsto commercialbanks,
this does not determine the composition of monetary authority liabilities. The
extent to which monetary authority liabilities take the form of outside money is
determinedby the relativedemandsfor various types of depositoryand nondepository accounts by the public. The key exogenous variablesset by the government,
therefore, are the increase in total real debt and its composition among treasury
bills, bonds, and other liabilities, not the supply of outside money. Total money
demand, along with the money multiplier,determinesthe level of outside money.
Finally, expected inflation is determined simultaneouslywith equilibrium real
balances and real government debt. In the steady state, given exogenous real debt
D =
creation, p -Z,andthecondition
p , we have D = 7i .
AND TESTING
2. MODELESTIMATION
Based on the discussionin the previoussection, we posit a long-termrelationship
for desired real money balances of the form
m* = k
+
aop
+
aly
+
a2Tre
+
a3il
+
a4i2
+
aSi3
+
a6v,
(4)
where all lowercase letters denote logarithms of variables; m* is desired real
balances;p is the price level;y is income; Tre iS expected inflation;v is the logarithm
of T-billvelocity,and il, i2, i3, arethe logarithmsof the interestratesdescribedin the
precedingsection. The price level is included in orderto test the zero-homogeneity
assumption (aO= 0)
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CHARLESW. CALOMIRISAND IAN DOMOWITZ : 83
Based on (4), the error-correctionrule for real balances iS4
Amt = ,Bo+
,BlApt + 25Tt
+
7(m-y)t-l
+
12Vt-l
+
+
+
8Tt-1
3tilst
+
+
9ilst-l
4ti2,t
+
+
loi2st-l
5ti3,t
+
+
6AVt
lli3st-l
(t
(S)
in which we have imposed long-term price homogeneity (aO= 0) and a long-term
unit income elasticity(a1 = 1).The validityofthese restrictionsis testablesimplyby
adding the terms a1p,-1 and a2y,-1 to the right-hand side of (5) and testing the
hypotheses a1 = 0 and a2 = 0.
Our data are monthly, extending from March 1972 to the end of 1981. A
complete descriptionof data sourcesis given in a Data Appendix,availablefrom the
authors. The choice of estimationperiod is dictatedto some extent by our interestin
the role of repurchaseagreementsin treasurybills. Prior to 1972 such repurchase
agreements were not very common. In recent years the growth of repurchase
agreementsin government bonds as well as bills makes treasurybill velocity a less
appealing proxy for repurchaseagreements. It also is the case that measuringthe
rate of indexation has become more difficult due to the growth in the number of
assets receiving"exchange"correction instead of "monetary"correction. We have
been unable to gather the right kind of information to sort out the potential
confusion over the types of correctionsin the most recent time periods.
Estimates of the coefficients in the money-demand relation (5) are reported in
Table 1. Instrumental variables estimation with respect to expected inflation is
common to both sets of estimates reported.5Version II estimates, however, are
correctedfor the potential endogeneityof the yields on bills of exchange and T-bills
and T-bill velocity, as suggestedabove.6
A series of diagnostic tests of model specificationare given in Table 2. Tests of
nonconstant residualvarianceare mixed. There is no evidence of dynamic ARCH
effects (Engle 1982),but a White (1980) test for heteroskedasticityoverwhelmingly
rejectsthe null of constant variance.As a consequence,all standarderrorsin Table 1
are corrected for general forms of heteroskedasticity,in order to draw proper
inferences.The heteroskedasticity-robusttest of Domowitz and Hakkio (1985)fails
to reject the null of no serial correlation in the regressionerrors, confirming the
4SeeDomowitzand Elbadawi(1987)and Domowitzand Hakkio(1986)for derivationsbasedon
single-period
andexpectedmultiperiodloss functions,respectively.
Thepreciserelationship
between
the long-termcoefficientsin (4)andthemodelcoefficientsin (5)is givenin CalomirisandDomowitz
(1987).
5 Allvariables
weresubjectedto twelfth-differencing
priorto estimationin orderto removea strong
stochasticseasonal.Seasonaldummieswerenot sufficient.The instrumental
variablesfor expected
inflationincludedlags of the inflationrate,of oil priceinflation,of (blackmarket)exchangerate
depreciation,
andof nominalT-bondgrowth,in additionto a quadratictimetrend.
6Thesevariablesareinstrumented
usingfourlagseachof i2, i3, and v, as wellas fourlagsof the
exchangeratedepreciation
andoil priceinflation.
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TABLE
1
MONEY DEMAND ESTIMATES
1
11
constant
0.078
(0.048)
0.072
(0.050)
Ap
- 0.159
(0.332)
- 0.094
(0.368)
re
0.946
(0.374)
0.963
(0.426)
Ail
- 0.034
(0.014)
- 0.027
(0.017)
Ai2
-
0.067
(0.035)
- 0.056
(0.087)
Ai3
-
0.048
(0.035)
0.039
(0.175)
-0.012
(0.011)
0.016
(0.039)
A
A
V
(m - y) l
-
0.130
(0.037)
- 0.124
(0037)
-1
- 2.019
(0.597)
- 2.116
(0.600)
il -t
- 0.007
- 0.006
(0.010)
(0.009)
0.036
(0.033)
- 0.028
(0034)
0.034
(0.026)
- 0.033
(0.027)
V-l
- 0.024
(0.012)
- 0.018
(0.013)
R2
0.463
0.391
SEE
0.026
0.027
i2 -I
-
i3,-I
-
'
NOTE: Dependent variableis the change in the log of realbalances, with a standarddeviation of 0.035. Estimatesbased on monthlydata,
from March 1972through December 1981. Heteroskedasticity-robuststandarderrorsare in parentheses.VersionI estimatesare obtained
from ordinaryleast squares; Version 11estimates are based on instrumentalvariables procedures.
TABLE
2
MODEL DIAGNOSTICS: MARGINAL SIGNIFICANCE LEVELS IN VERSIONS
1
Heteroskedasticity
(White)
Heteroskedasticity
(ARCH)
Serial correlation
Hausman
statistic
Chow statistic
Zero exchange
rate effect
Price homogeneity
Ullit income elasticity
0.003
0.84
0.34
0.12
0.45
0.94
0.31
0.22
I AND II
11
0.001
0.80
0.46
0.56
0.77
0.97
0.96
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CHARLESW. CALOMIRISAND IAN DOMOWITZ : 85
appropriatenessof the dynamic specification.7One cannot rejectthe stabilityof the
model parametersat any reasonable level of statistical significance, based on a
Chow (1960) test.
A Hausman(1978)statisticis used to examine the potentialendogeneityof T-bill
yield and velocity, as well as the yield on bills of exchange. The marginalsignificance
level associated with a test of the equivalenceof parameterestimatesacrossversions
I and II is 0.12, and we would fail to rejectthe exogeneity of these variablesat the 10
percent significancelevel. A closer examination of the components of the statistic
suggest that it is the exogeneity of the T-bill rate that may be questionable. T-bill
velocity is clearly exogenous on statisticalgrounds.
The price homogeneity and long-term unit income elasticityrestrictionscannot
be rejectedat reasonablelevels of statisticalsignificance.Point estimates of unconstrainedincome and price elasticitiesare 0.93 and 0.97, respectively,based on the
version I results.Instrumentalvariablesestimatesare 0.90 and 0.95 for income and
price effects. The point estimates suggest that any violation of the unit restrictions
also is unimportantin economic, as well as statistical,terms. These resultsare quite
similar to the income elasticities reported in Blejer (1978), Vinals and van Beek
(1979), Cardoso (1983), and Khan (1979, 1980). Leiderman(1980) reportsincome
elasticitiestwice as large.
We also impose a zero restrictionwith respectto the effect of exchange rates on
the money demand function, as suggested by our analysis of target balances in
section 1. Dornbusch et al. (1983) arguethat foreign currencysubstitutionis not an
important element in money demand.8In order to test this hypothesis we add the
rate of exchange rate depreciationas an opportunitycost to the set of interestrates
in the model. As Table 2 shows, the hypothesis of zero explanatory contribution
cannot be rejected. Point estimates of exchange rate effects are quite small in
magnitudeas well. Blejer(1978)found a largeand significanteffect of exchange rate
depreciationin his study of Brazilianmoney demand. Blejer'suse of annualdata and
his assumption of adaptive inflation expectations may explain the difference
between his finding and ours. If currency depreciation predicts inflation (as our
rational expectations forecasts of monthly inflation indicate it does), then by
constrainingannual expectations of inflation to depend only on the past rate of
inflation,Blejermay have misinterpretedthe indirectrole of exchangeratedepreciation (an inflation forecaster)as a direct currency-substitutioneffect.
The coefficients reported in Table 1 are not directly interpretablesince they
representcombinations of structuralcoefficients, including long-term elasticities
and relativeadjustmentcosts. We simply note that the estimatedcoefficientsare of
the expected sign, with the exception of those on the change in the T-billrateand the
change in T-bill velocity in version II, which are estimated with standard errors
7StandardLagrangemultipliertestsfor serialcorrelationresultedin evenhighermarginalsignificancelevels.All otherstatisticsreportedsubsequentlyhavebeencorrectedfor non-constanterror
variances.
8Empiricalevidenceon this point is given in Domowitzand Hakkio(1986)for industrialized
countriesandin DomowitzandElbadawi(1987)fora countrywithrelativelycrudefinancialmarkets.
Thesereferences
all supporttheviewthatforeigncurrencysubstitution
is notimportantformodeling
domesticmoneydemand.
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86 : MONEY,CREDIT,AND BANKING
TABLE 3
EQuATIoN,
IN REALMoNEY-DEMAND
COEFFICIENTS
LoNG-TERM
LAGS
ANDMEANANDMEDIANADJUSTMENT
version
I
II
Variable
IndustrialProduction Index (Y)
IndustrialProduction Index
I
Expected Monthly Inflation (re)
II
Expected Monthly Inflation
Long-Term
Coefficient
la
la
.155
(0.037)
-0.171
MeanAdj.Lag
8.5 months
8.8 months
Median
Adj.Lag
2.8 months
2.7 months
7.1 months
2.7 months
7.5 months
2.7 months
1.9 months
1.4 months
2.2 months
1.5 months
(0.040)
I
II
I
II
I
II
Monthly Rate of Indexation (il)
Monthly Rate of Indexation
.054
(0.072)
-0.048
(0.066)
Monthly Bill of Exchange Rate (i2)
-0.277
4.8 months
2.3 months
Monthly Bill of Exchange Rate
(Instrumented)
(0.270)
d.226
(0.302)
5.1 months
2.3 months
5.3 months
2.4 months
8.2 months
2.8 months
Annual T-Bill Yield (i3)
Annual T-Bill Yield (Instrumented)
d.262
(0.153)
-0.266
(O. 160)
2.7 months
7.2 months
-0.185
(0.078)
2.8 months
8.0 months
d.145
T-Bill Velocity (Instrumented)
II
(0.094)
inflation.
except
expected
in logarithms,
areexpressed
NOTE: Allvariables
fromunityatthelo percent
different
were0.93ando.so,insignificantly
estimates
tobeunity.Unrestricted
a Coefficients arerestricted
level.
significance
I
T-Bill Velocity (v)
exceeding the coefficients. A detailed derivation of the long-termelasticities the
coefficientsin equation (4)- and the mean and medianadjustmentlags for disturbances to each of the argumentsin equation (4) is given in Calomirisand Domowitz
(1987). These are reportedin Table 3.
Our long-term elasticities with respect to expected inflation are smaller than
many of those reported in other studies. For example, Blejer (1978) reports a
long-termcoefficientof-0.35, comparedto our estimatesof-0. 16and-0. 17.This
may be due to the inclusion of asset yields and the use of forecastedinflation rather
than lagged or actual inflation.
Withrespectto the dynamics,previousstudies'mean and medianadjustmentlags
have been large relative to those reported in Table 3. This is because estimates of
speeds of adjustmenthistoricallyhave been based on partialadjustmentmodels (see
Hendry 1980for discussion).In contrast,mean lag estimateshereare quitelow, with
adjustmentlags ranging from 8.5 months for an income shock to 2 months for a
shock to the rate of indexation.The lag distributionis skewed,however,and median
lags are much shorter. Fifty percent of the adjustmentto an income shock takes
place within less than 3 months, for example.
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CHARLESW. CALOMIRISAND IAN DOMOWITZ : 87
3. EVIDENCE OF THE ENDOGENEITY OF NOMINAL VARIABLES TO DEFICITS
Previous studies of the relationshipbetween money and inflation (e.g., Hanson
1980)document a unique feature of the Brazilianeconomy. Changes in money do
not predict changes in the price level, but changes in the price level do predict
changes in money. This finding and the large increasesin the growth rate of prices
relative to money (rising velocity) have led the Brazilianauthorities to claim that
"cost-push"influences (like the oil price rises of the 1970s)have been the source of
high Brazilianinflation.
Ourmodel is consistentwith a laggingresponseof nominalbalancesto changesin
prices, and the rising velocity of money, but our interpretationof the source of
Brazilian inflation is very different from the cost-push view. Nominal money in
Brazilis endogenous to real asset demands and nominal governmentdeElcits.Real
money demand has been stable and responsive.Rising deficitsare mainly to blame
for secular rises in money, interest rates, prices, and exchange rates; thus money
should not predict or cause changes in other nominal variables,as it would if the
money stock were supplyZetermined.
In Table 4 we report the results of a VAR (Vector Autoregressive)model for
Brazil, which includes money, prices, the bill-ofwxchangerate, the industrialproduction index, and public holdings of T-bills and indexed bonds. Innovations in
nominal government debt aggregates serve as proxies for innovations in government deElcits.
The exogenous variablesare time, time squared,eleven monthly dummy variables, a constant term, and lagged oil prices. All others are lagged endogenous
variables. Five-month lag structureswere used in the estimations. Treasurybills,
TABLE
VAR
4
RESULTS
F-Tests for Inclusion of Lags
Dependent Variables:
Lagged
Values of:
T
IB
E
i2
Y
P
M
T
0.00
0.24
0.96
0.52
0.42
0.44
0.11
IB
0.90
0.00
0.89
0.21
0.90
0.55
0.50
E
0.85
0.01
0.01
0.25
0.21
0.28
0.38
t2
0.09
0.35
0.54
0.01
0.87
0.04
0.08
Y
0.33
0.29
0.76
0.99
0.03
0.76
0.51
P
0.48
0.05
0.10
0.71
0.04
0.00
0.47
M
0.21
0.00
0.01
0.00
0.06
0.01
0.00
Forecast VarianceDecompositlon (2Smonth horizon):
Forecast
Varianceof:
T
IB
E
i2
Y
P
M
T
IB
E
i2
36.4
3.5
2.1
6.1
12.4
2.0
17.5
10.5
53.9
55.4
14.5
10.4
35.2
18.5
17.6
19.7
29.0
11.0
16.3
10.4
6.3
12.4
5.4
1.9
41.2
6.1
9.7
25.3
Y
3.8
6.1
3.6
5.6
44.0
7.5
16.1
P
M
7.2
5.3
6.6
10.8
5.0
27.1
3.1
12.1
6.2
1.5
10.8
5.9
8.0
13.3
NoTE: Variablesare as defined in the text. All variablesexcept i2 are expressed in logarithms. Five monthly lags of each endogenous
variables are included in the estimation equations, as well as five lags of oil prices, monthly dummies and a quadratic time trend.
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88 : MONEY,CREDIT,AND BANKING
whichplay an importantrole in repurchaseagreements,are separatedfrom treasury
bonds. All variablesexcept the bill-ofwxchange rate are in log levels. Table 4 gives
an account of the avenues of predictionin the model. We Elndthat money does not
predict prices, while bonds are one of the strongest predictorsof prices. The bond
supply is also a signiElcantpredictorof the money supply and the exchange rate. At
the same time, no other variableis important in predictingbonds.
One way to measurethe importanceof the effects of each variablein the system
on every other variableis the decomposition of forecastvariance.This describesthe
percentageof a variable'sforecastvariance,over increasingtime horizons, that can
be attributed to innovations from each variable in the system. In order to run
simulations of the system's responses to shocks and derive forecast-variance
decompositions,it is necessaryto ordercontemporaneousshocks when orthogonalizing the system. We order the variablesfrom most "exogenous"to most "endogenous" as follows: T, IB, E, i2, Y, P, M.9 E is the black-marketexchange rate.
Table 4 illustratesthe importance of public debt for E, i2, P, and M. Shocks to
bonds produce positive responses to all nominal variables. Bond innovations
account for 35 percent of the long-term (20 month) forecast variance of the price
level and 55 percent of the exchange rate, while money innovationsaccount for 1.5
percentand 8 percent of the respectiveforecast variancesof the exchange rate and
price level.
It is interestingto note that price innovations are neitherstatisticallysigniElcant
nor economically important in accounting for changes in bonds. This may seem
surprising,given that bonds are indexed partiallyto inflation.This resultreflectsthe
fact that past levels of bonds incorporate previous indexation adjustments. The
importance of exchange rate innovations for bonds and the price level reflects
expectations of inflation, and consequently,indexation.
These resultsare consistent with our model in which governmentdeElcitpolicies
and real asset demand functions together determine nominal money balances,
which adjust with a lag to innovations in deElcits,interestrates and inflation.
The central role of deElcitsin generatinginflation may help explain the recent
collapse of the "CruzadoPlan."In the absenceof consistentreductionsin the deElcit
'Yorcingprocess,"pricecontrols alone will not be successfulfor reducinginflationin
the long term. Only if government spending is reduced and direct taxation is
increasedwill Brazil be able to stem its inflationarytide.
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9The resultsare quite robust to variations in the ordering. In particular,when money was switched
from last to first, the only important difference in the simulation results was the percentage of each
other's forecast variance which Af and i2 impulses explain.
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All use subject to JSTOR Terms and Conditions
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