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Illiquidity, Consumer Durable Expenditure, and Monetary Policy

In the literature on consumer durable
expenditure,1 monetary policy has a major
impact either through interest rate 2 or
liquid asset (real balance) effects. The theoretical justification for the inclusion of
liquid assets as an important determinant
of consumer durable expenditures is not
particularly strong,3 and results with this
variable have been mixed.4 Yet, even
though there is a solid theoretical basis for
monetary policy effects through interest
rates, empirical econometric work has
rarely found these effects to be substantial.5
One possible conclusion from research
* Teaching assistant, department of economics,
Massachusetts Institute of Technology. I want to thank
Stanley Fischer, Paul Joskow, Franco Modigliani,
William Poole, and the participants in the Money
Workshop at M.I.T. for their helpful comments. I would
also like to thank Roger Hankin for computational assistance. This research was conducted while I was the
recipient of a NSF Graduate Fellowship. Further research
support has been provided by the Federal Reserve Bank
of Boston. The evidence and conclusions set forth here
are solely my own, and do not indicate concurrence by
the Federal Reserve System.
See Franco Modigliani; Michael Hamburger; F.
Thomas Juster and Paul Wachtel (1972a); Michael
McCarthy; Albert Hirsch, Maurice Liebenberg, and
George Green; Ta-Chung Liu and Erh-Cheng Hwa; and
Otto Eckstein, Edward Green, and Allen Sinai.
Classified with interest rate effects are the effects of
installment credit terms.
One justification for the inclusion of liquid assets in
consumer expenditure equations is found in Arnold
Zellner, David Huang, and L. C. Chau.
In none of the models mentioned in fn. 1 does the
liquid asset variable enter significantly and with the right
sign—indeed it often enters with the wrong sign—in
both equations for the autos and parts and nonauto
components of consumer durable expenditure.
Hamburger's study seems to be the only piece of
empirical work where these effects are substantial. Yet,
he only finds these powerful effects when interest rates
enter his equations with very long lags.


in this area is that monetary policy has
only a marginal effect on consumer durable
expenditures. Another possibility, however, is that channels of monetary policy
as yet unexplored might be a crucial determinant of this type of expenditure.
This paper studies the neglected illiquid
aspect of the consumer durable asset. It
finds that increased consumer liabilities are
a major deterrent to consumer durable
purchases and increased financial asset
holdings a powerful encouragement. The
results show that monetary policy has a
strong impact on consumer durable expenditure through two additional channels
of monetary influence: 1) Monetary policy
affects the price of assets in the economy.
Consumer financial asset holdings, thereby
affected, influence expenditure on durables.
2) Past monetary policy will have affected
the cost and availability of credit, thus
influencing the size of consumers' debt
holdings and hence consumer durable expenditure.
The paper proceeds in the following
way: the next section develops a model
which determines the effects of consumer
durable illiquidity on the desirability of
this asset; the second section contains aggregate time-series tests of this model; and
the final section discusses the implications
for monetary policy and contains concluding remarks.
I. Illiquidity of the Consumer
Durable Asset
One aspect of the consumer durable asset that distinguishes it from financial
assets is its illiquidity. Well-developed capital markets exist for most financial assets,

VOL. 66 NO. 4


and cash can be generated with a minimum of cost in time, money, and effort by
selling them in their near perfect markets.
Capital markets for used consumer durables are, on the other hand, highly imperfect. Durable goods are very heterogeneous, and much information which is
costly to obtain is needed to determine
their value.6 Also the bulk and difficulty in
handling of durables leads to high transaction costs in their purchase or sale.
These transaction and information problems lead to a wide spread between the
price the consumer receives from selling
his used consumer durable and its value in
A simple two-period model of the effects
of consumer durable illiquidity on the desirability of this asset is developed below.
It is shown that the nature of markets for
consumer durables forces the consumer to
take account of his balance sheet status,
i.e., his debt and financial asset position,
as well as the riskiness of his income
stream, in determining the desired level of
his consumer durables stock.
Assume that a consumer buys a unit of
durables with price equal to unity at the
beginning of period one. The durable's inuse value at the end of the period would be
1 — d, where d is the depreciation rate.9 Yet,
if the consumer suffers a shortfall in income so that the durable good has to be
sold in a distress manner, its full value can6

For example, how well has the owner treated his
durable, has it been damaged, how frequently has it been
used, was it a lemon to start with, etc.
The value-in-use is the present discounted value of
the durable'sflowof services.
To see why costly information would lead to a spread
between selling price and in-use value, see George
Akerlof. In an extreme case no organized market might
exist as a result of information problems. The absence of
organized markets for many types of used consumer
durable goods is quite common.
In the case of a durable where there is a planned
trade-in, the expected costs incurred in the trade-in—
transactions and otherwise—are included in depreciation.
The value of the durable at the end of the period reflects
these costs.


not be realized. Its illiquidity stems from
the imperfect nature of the used consumer durable capital market. The degree
of this illiquidity will be described by the
, which is the fraction of
in-use value that can be realized from a
distress sale. This formulation is quite
general: it is not dependent on any specific
type of illiquidity loss; it includes the loss
from a low sales price as well as from transaction costs. If, as a result of an income
shortfall, a distress sale of the durable at
the end of the period is required to raise
cash, then the realized value of the durable
at the end of the period will be
where q is less than one.
If there is no distress sale, the oneperiod opportunity cost
of holding a
durable rather than a financial asset will
But if there is a distress sale as a result of
an income shortfall, then:

where 10

= one-period opportunity cost

The opportunity cost in equation (2) assumes that
a consumer cannot borrow to cover his income shortfall
or that the cost of borrowing over and above the yield on
financial assets is more than
. It is well
known thatfinancialintermediaries are more than happy
to make loans to consumers when they least need it and
are extremely reluctant to make loans to consumers when
they are infinancialtrouble. If thefinancialintermediary
does make a loan at all to a consumer with an income shortfall, it charges a very substantial premium to
compensate for the increased risk. Thus the assumption
inherent in equation (2) is quite reasonable. If the difference between the borrowing cost and the yield on financial assets is less than
, the consumer will
borrow instead of selling his consumer durables. This can
be incorporated into the above model by replacing
with the spread between the distress borrowing rate and the yield on financial assets. This leads
to the same results as found in the text.
The reluctance of financial intermediaries to lend to
consumers in financial trouble explains why most consumers hold debt and financial assets at the same time,
even if borrowing costs for the consumer not suffering
financial distress are somewhat higher than the yield on



of holding a durable, and r= one-period
return on financial assets (which is assumed certain).
We can now view the opportunity cost
of holding durable goods in an uncertain
world with a Tobin-Markowitz meanvariance framework. If the probability of
making a distress sale is p and not making
a distress sale is 1 —p, then


=the square root of the income
variance, with

since k is usually assumed to be less than
Debt service is a positive function of the
consumer's liabilities at the beginning of
the period, hence
where E and Var are the expectation and
variance operators, respectively.
A distress sale occurs whenever consumption11 plus debt service (interest plus
amortization) is larger than income, plus
readily available financial assets; i.e., when

DS + CON -

Y - FIN > 0

where DS= debt service
CON= consumption
Y— disposable income
FIN = holdings of financial assets
The permanent income hypothesis implies that

where k=the propensity to consume out
of permanent income, and
= expected
average (permanent) income. If income is
a normally distributed random variable,
then using the standard normal distribution formula we may write:
financial assets. When a consumer suffers a drop in
income,financialassets are a buffer that help prevent the
consumer from taking losses either by selling his durables
or borrowing at inflated rates to raise cash; thus the
consumer will not try to minimize his borrowings by
holding no financial assets as he would in a world of
absolute certainty and perfect capital markets.
Since a distress sale can be avoided at a relatively
low cost by a reduction in consumer durable expenditure,
consumption, not consumer expenditure, is the relevant
variable for the necessity of a distress sale.

where DEBT=liabilities
of the period. Now:

at the beginning

VOL. 66 NO. 4


If the probability of a distress sale is
less than one-half
for consumer
durables, which would certainly seem to be
the case for most individuals in our economy, then12

In a Tobin-Markowitz mean-variance
model, both a lower expected opportunity
cost and a lower variance are preferred.13
Therefore, a consumer durable is a more
desirable asset: the lower the debt holdings, the higher the financial asset holdings, the lower the variance of income, and
the higher is expected income in this
As can be seen in an appendix available from the
author, the assumption that p is less than one-half is
certainly not needed for the debt and financial asset
results obtained here.
If the consumer has a diversified portfolio, then the
capital asset pricing model applies; he prefers a lower
mean opportunity cost and a lower covariance with the
market return. If the correlation of the opportunity cost
of holding a durable and the market return is positive
and reasonably constant, then a lower variance of the
opportunity cost is preferred as in the simple meanvariance model used above. Richard Bower and Donald
Lessard indicate that for most situations the simple
mean-variance model usually leads to the same decisions
as the capital asset pricing model.
The model above is quite simple and gives a nice
neat result, yet it does make the unrealistic assumption
that consumption cannot be lowered below its desired
level to meet the problem of an income shortfall, or that
it would be more costly to do so than to incur a loss from
distress selling a consumer durable. Furthermore, the
mean-variance model used here requires special assump-


II. Time-Series Tests of the
Liquidity Model
A stock adjustment model incorporating
the results of the "liquidity" model of the
previous section is developed here. It is
tested on quarterly aggregate time-series
data for consumer durables expenditure
and its two component parts: autos and
parts expenditure, and nonauto consumer
durables expenditure. The models are
estimated over the period 1954-I through
1972-IV, with the exclusion of quarters in
which there were auto strikes, i.e., 1964-IV
to 1965-II and 19704V to 1971-II.15 All
quantities are in real per capita terms
(thousands of 1958 dollars per capita) with
flows as seasonally adjusted annual rates.16
A. The Model
The literature views a consumer durable
as an asset in the portfolio which yields a
return of consumption services; the consumer derives benefits from the services of
the stock, not from the flow of durable
purchases.17 The consumer thus desires a
tions which have been objected to in the literature. A
more general model, found in an appendix available from
the author, has been developed which does not rely on
the special assumptions of the mean-variance model and
allows the consumer to meet an income shortfall by
lowering his consumption below its desired level. The
results for the effects of debt andfinancialasset holdings
on the desirability of the consumer durable asset are the
same in this model as in the mean-variance model presented above. The more general model is not used here
because its exposition is not as simple, and because the
role of income stream riskiness is not as clear.
Strong strike effects are felt in both the quarter of
the strike and the quarter following. Use of first-order
serial correlation corrections necessitates excluding the
second quarter following the strike from the sample
period as well as the two previous quarters in the consumer durables and autos and parts estimations. These
quarters were also excluded for the nonauto consumer
durables estimations because aberrations in the auto
sector might have an impact on nonauto durable purchases. In fact, model estimates for the nonauto consumer durable sector were not appreciably affected when
the excluded quarters were included in estimating the
The sources of these data are described in another
appendix available from the author.
See Arnold Harberger, Gregory Chow, Modigliani,
Richard Stone and D. A. Rowe, and Juster and Wachtel



FIN= real per capita gross financial
asset holdings of households
(includes demand deposits
plus currency, time and savings deposits, bonds, corporate equity, life insurance and
pension funds, and other miscellaneous assets)—beginning
of quarter,
EA = additive error term.

certain stock of durables which is a function of permanent income and the user
rental cost of capital. The liquidity model
developed in the previous section indicates that, in addition, the desired durables
stock is a function of the value of the consumer's debt and financial asset holdings
at the beginning of the period. Therefore:

where K* — real per capita desired stock of
Y p =real per capita expected average (permanent) income,
CA PC — user rental cost of consumer
durable capital18

RCB = Moody's AAA corporate bond
D = annual depreciation rate,19
PCD = consumer durables implicit
price deflator,
PCON= consumption implicit price deflator,
DEBT=real per capita debt holdings
of households—beginning of
The user rental cost of consumer durable capital
used here is completely analogous to the user rental cost
of capital in the investment studies of Robert Hall and
Dale Jorgenson and of Charles Bischoff. The interest rate
in the formula above is a nominal interest rate, not a
real interest rate as would be appropriate in the HallJorgenson formulation; thus the effect of inflation on
consumer durable expenditure is not incorporated into
this model. Attempts were made to estimate the effect
of inflation on consumer durable expenditure and include
it in the model, yet experiments with varied distributed
lags of past inflation rates proved fruitless; no significant
effects could be obtained. This is not surprising for the
effect of inflation is by no means clear. On one hand, with
constant nominal interest rates inflation lowers the user
rental cost of capital and encourages durable expenditures. Yet evidence from consumer surveys indicates
that inflation increases consumers' perceptions of uncertainty (see Juster and Wachtel, 1972b), and this has
a depressing effect on consumer durable expenditures.
The assumed depreciation rate used in calculating
the capital cost measure for all consumer durables is .20,
while it is .25 for autos and parts, and .15 for nonauto
consumer durables.


When expected income is high, and the
desired durables stock is high, a change
in the user capital cost should cause a
larger dollar change in the desired stock of
durables. Thus, equation (18) is linearized
with the coefficient of permanent income a
linear function of the user rental capital
cost,20 i.e.,

Consumer durable expenditure is modeled with the stock-adjustment or so-called
flexible-accelerator model which views
consumers as adjusting only slowly to their
desired stock of durables. The change in
the stock, i.e., net investment, is only a
fraction, X, of the gap between the desired
and actual stock at the beginning of the
period. Net investment is also viewed as a
function of transitory income because:
1) some portion of transitory income and
hence saving should be reflected in consumer durable purchases; and 2) transitory income is a proxy to some extent for
perceptions of income variance21'22 which
This assumption is not critical to our argument. If
K* is alternatively assumed to be a linear function of the
right-hand side variables in (18), i.e.,

the fit of the estimated model and the asymptotic
change hardly at all, and the important empirical results
of this paper still hold.

Transitory income is a cyclical variable which is
related to the probability of a worker losing his job and
suffering an interruption of his normal income stream.

VOL. 66 NO. 4


the liquidity model indicates affects the
desired stock of durables and hence net
investment.23 Therefore:

K = real per capita stock of durables
at the end of quarter,
X=the quarterly adjustment rate,
YT = real transitory income per capita,
When transitory income is low, workers have a high
probabilty of being laid off and have a larger income
variance, and when it is high, workers have a low probability of being laid off and have a correspondingly lower
income variance.
The unemployment rate is also a cyclical variable
that reflects the probability of losing one's job and is
related to income stream variance. If transitory income
is excluded from the expenditure model and the unemployment rate is used as a proxy for income variance in
its place, it enters with the appropriate negative sign
(indicating that higher income variance depresses consumer durable demand). It is statistically significant at
the 5 percent level or higher in regression models for all
consumer durables and its two component parts: nonauto
consumer durables and autos and parts. The debt and
financial asset variables results are not qualitatively different when unemployment is used in the expenditure
models instead of transitory income.
Attempts to find further measures of perceived income variance were unsuccessful. The unemployment
rate, the Survey Research Center (SRC) consumer sentiment index, a filtered version of this index (see Juster
and Wachtel, 1972b), a crude measure of perceived risk
in the financial markets using yield spreads between low
grade corporate bonds and comparable government
securities, and calculated income variance from past
data, were all tested in the equation (22) model shown
here. Only the unemployment rate and the filtered SRC
index proved to be statistically significant in any regression equation. Both of these variables were significant in
the autos and parts regressions, yet the transitory income and adjustment speed coefficient took on unreasonable values. Furthermore, both variables had the wrong
sign in the nonautos regression. The failure to find
further measures of consumers' perceptions of income
variance is not a severe problem. The estimated effect of
financial asset holdings on the desired consumer durables
stock should in any case reflect perceived income variance effects because of high correlation of the perceived
variance and asset measures. When perceived income
variance increases, a higher risk premium would probably
be used in discounting the earning streams of equity.
This causes a lower valuation of equity; thus the value
of financial assets falls. A strong negative correlation
between the gross financial assets measure and perceived
income variance is thus expected.


EB= additive error term,
and subscripts refer to the time period of
the K variable.
Consumer durable expenditures, or equivalently, gross investment in consumer durable goods, equals the sum of net investment and replacement. Assuming a quarterly replacement rate of :
where EXP=real per capita consumer
durable expenditures at an annual rate.
Combining equations (18) through (21)
we derive the model to be estimated:

where u— additive error term=
The signs of all the coefficients of equation (22) are easily determined. The coefficients on permanent and transitory income
should both be positive because increased
permanent or transitory income encourages consumer durable purchases.24 Increased user capital costs should discourage purchase of consumer durables; this
implies that
is less than zero. The
lagged stock coefficient will be negative if
the speed of adjustment is higher than the
replacement rate—the usual case.
The results of the previous section indicate that illiquidity of the consumer durable asset should lead to a positive FIN
coefficient and a negative DEBT coefficient in the above model. Changes in the
value of financial assets for the wealthy,
for whom liquidity is not a problem, might
have a smaller impact on consumer durable expenditure than for the middle or
lower income groups. For this reason, the
The transitory income coefficient should be positive
not only because transitory income might be saved in the
form of consumer durables, but also because a rise in
transitory income indicates that consumers' income variance may have declined, thus increasing the desired
stock of durables and durable purchases.



unequal and highly skewed distribution of
financial asset holdings in this country
would tend to sharply lower the aggregate
financial assets coefficient in a model estimated on aggregate time-series data. On
the other hand, consumer liabilities are
distributed far more equally than financial
assets; thus the coefficient on consumer
liabilities should still retain a high value
in time-series estimations. Even though the
liquidity model does not imply that for an
individual the debt coefficient should be
markedly larger in absolute value than the
financial assets coefficient, this result might
be expected in time-series estimates of
these coefficients which reflect the distribution effects described above.
B. Empirical Estimates
Equation (22)—whether it be estimated
for expenditures on all consumer durables,
or for autos and parts and nonauto consumer durables expenditures—is just one
equation in a simultaneous system; thus
simultaneous equation bias will result from
ordinary least squares estimation. In the
above model this bias would be especially
severe for the debt coefficient.25 To avoid
least squares bias, an instrumental variable technique has been used.26 Strong
serial correlation is evident in all the re25
Ordinary least squares estimates of the debt coefficient would be severely biased upward if the error term
is positively serially correlated—the usual case. A positive error last period would imply a positive error in the
current period, while increased durable purchases last
period—a result of the positive error term—would lead
to increased debt holdings at the beginning of the current period. The debt variable and the error term would
thus be positively correlated, and this would lead to an
upwardly biased ordinary least squares coefficient estimate. A comparison of the ordinary least squares and
instrumental variables estimates of equation (22) indicates that the bias in ordinary least squares estimates is
of the predicted direction and is quite strong.
The list of instruments includes unborrowed reserves
at member banks plus currency outside of banks, the
discount rate, exports, federal government expenditures,
the effective rate of personal income tax, these five variables lagged one period, the constant term, and population.



gression equations, and to achieve efficient
estimates a first-order serial correlation
correction has been made using Ray Fair's
method and the appropriate additional instruments.27, 28 The results for each sector
are denoted by superscripts: D for all consumer durables; A for autos and parts; and
NA for nonauto consumer durables.
The estimates for consumer durables are
as follows, with asymptotic t-statistics in
parentheses. The coefficient on u-1 is the
first-order serial correlation coefficient.

R2 = .9932; Durbin-Watson = 1.90; Standard
Error = .007529.
The results are good. The coefficients
of the debt and financial asset variables
have the signs hypothesized by the liquidity model and are highly significant; the
coefficients are over four times their respective asymptotic standard errors. The
depressing effect of debt holdings on consumer durable purchases is quite substantial; for every $1 of debt held at the beginning of the quarter, durable purchases at
an annual rate will be decreased by 22 .
The value of financial asset holdings has a

Except for the lagged stock coefficients, regression
estimates where there was no correction for serial correlation were not appreciably different from the corrected
regression estimates. The serial correlation corrected
regressions exhibited a higher adjustment speed of desired to actual stocks.
Ordinary least squares estimates using a CochraneOrcutt technique for autocorrelation correction are provided in an appendix available from the author. Qualitatively the results are similar to those in the text (i.e.,
signs and t-statistics), though coefficient estimates sometimes differ by as much as 30 percent.

VOL. 66 NO. 4


significant positive effect on the demand
for durables, though, as might be expected,
it is not as strong as the depressing effect
of debt; an extra dollar of financial assets
held at the beginning of the quarter leads
of increased durables purchases.
In addition, the
, and CAPCD
coefficients are all significant and of the
expected sign in the estimated equation
above. The magnitudes of these coefficients
are also quite reasonable; 27 of a $1 increase in transitory income is spent on
consumer durables, while a $1 increase in
permanent income leads to somewhere in
the neighborhood of 34 of increased durables expenditures. At the means of the
sample data the interest rate elasticity of
consumer durables expenditure is —.14,
while the price elasticity is —.71. The
lagged stock coefficient implies that approximately 6 percent29 of the discrepancy
between desired and actual stocks of durables is made up within the quarter; this
is an annual adjustment rate of 22 percent.
The consumer durables demand model
presented so far only allows for lags in the
adjustment of actual to desired consumer
durable stocks; i.e., no decision lags are
allowed in the consumer's determination of
his desired stock. This assumption seems
rather naive. The consumer may acquire
information on his user rental cost of durables slowly, and thus his decision on his
desired stock of durables may be influenced
by past as well as present user rental costs.
Capital gains or losses may not be considered fully part of financial assets until
they are realized. Movements in common
stock prices, which lead to unrealized
capital gains or losses in the short run,
should not have their full impact immediately; instead, the valuation of common
stock would affect the desired consumer
durables stock with a distributed lag.
This assumes a quarterly replacement rate of
.05625, which is the depreciation rate used in computing
the consumer durables stock.


To test for the possibility of the lags
described above, experimentation with
polynomial distributed lags of the user
rental cost variable and stock market financial assets have been pursued. There is
no improvement in the standard error of
the regression or asymptotic t-statistics
from a lag on the capital cost variable. It
seems that the consumer does not take
long to acquire information on his cost of
capital. On the other hand, a substantial
improvement in fit is obtained when the
value of stock market assets affects the
desired stock of durables with a distributed
lag. The liquidity model implies that there
should be no differences in the effect of
stock market and non-stock market assets
on consumer durable desirability; thus the
sum of the lagged stock market asset coefficients should be equal to the coefficient
of unlagged non-stock market financial assets. Applying this a priori equality as a
constraint,30 experiments with polynomial
distributed lags constrained to be zero at
the tail resulted in an endpoint constrained, second degree polynomial with a
four-quarter lag having the best fit (lowest
standard error of the regression). The result using instrumental variables and
Fair's method is:

The null hypothesis that this constraint is valid cannot be rejected at the 5 percent level. This hypothesis
was tested with a two-tailed asymptotic t-test. The
asymptotict-statisticsequals .3276 while the critical t at
the 5 percent level is approximately two.



R2 = .9940; Durbin-Watson = 2.01; Standard
Error = .007104.
STK= real per capita value of households' stock market asset holdings—beginning of quarter.
NSFIN = real per capita non-stock market financial asset holdings
of households—beginning of
The lag pattern of equation (24) has a
desirable shape, with a stronger impact on
durables expenditure from more recent
movements of stock market asset holdings.
Furthermore, the overall impact of gross
financial consumer assets on durables expenditures is larger in the lagged equation
(24), than in the unlagged version (23):
the overall financial assets coefficient is
.0632 in (24) vs .0453 in (23). The debt coefficient also increases in absolute value in
(24); $1 of increased debt holdings now
leads to a 31 decrease in durables purchases. The YT and YP coefficients still
have reasonable magnitudes in this regression and are significant at the 1 percent
level, while the lagged stock coefficient
now implies that over 12 percent of the
discrepancy between desired and actual
stocks is made up within the quarter—an
annual adjustment rate of approximately
40 percent. This speed of adjustment is
quite plausible and is in the middle of the
range of estimated adjustment speeds in
other consumer durable studies.31

See Harberger.


A striking result of allowing a distributed lag on stock market financial assets is the increase in absolute value of the
capital cost coefficient and the rise of its
asymptotic t-statistic to a value over three.
In this model, the user rental cost of capital, and hence interest rates, has a strong
and significant effect on consumer durable
purchases. At the sample means the interest rate elasticity of consumer durable purchases is — .20.
To put the regression results of (23) and
(24) in perspective, it would be worthwhile to compare them to results from a
regression which does not include the debt
and financial asset terms which are implications of the liquidity model. Instrumental variable estimates using Fair's
method for this "standard" stock-adjustment consumer durables model are as follows :

R2 = .9919; Durbin-Watson =1.75; Standard
Error = .008111.
The regression results of equation (23)
and especially (24), which incorporate the
liquidity model, are much superior to the
results of the standard regression (25).
The fit is better and the autocorrelation
coefficient—an indicator of specification
error—is far lower. The YT and YP coefficients are not as statistically significant
in the standard regression, and the speed of
adjustment—a quarterly rate of 7 percent—is somewhat low.
* The model of equation (22) has also
been estimated for the autos and parts, and
the nonauto consumer durables sectors
separately. Regression estimates using instrumental variables and Fair's method

VOL. 66 NO. 4




Instrumental Variables Estimates Using Fair's Method
Dependent Variable: EXPA

Instrumental Variables Estimates Using Fair's Method
Dependent Variable: EXPNA

Coefficient of







Coefficient of
Constant Term


Standard Error


Constant Term









Standard Error




Note: p= First-order serial correlation coefficient. All
other variables are as defined in the text. Asymptotic
t-statistics in parentheses.

appear in Tables 1 and 2. Experiments
with endpoint constrained, polynomial distributed lags were also carried out for
these sectors, and, as in the case for all
consumer durable expenditures, the best
fits were obtained with a four-quarter endpoint constrained, polynomial distributed
lag on stock market assets. The constraint
that the sum of the STK coefficients should
equal the coefficient on NSFIN was imposed.32 The estimates incorporating lags
The null hypothesis that this constraint is valid
cannot be rejected at the 5 percent level for either sector.

on stock market asset holdings also appear in Tables 1 and 2.
The results for both the autos and
parts and nonauto consumer durable sectors are excellent. The debt and financial
asset variables are of the right sign and
are significant in all cases. The lag pattern
of stock market assets in the lagged versions of the model is very similar in both
sectors and has a sensible shape; more
recent movements in the value of stock
The asymptotic t-statistic for the auto and parts and
nonauto consumer durables regressions were .4294 and
1.1728, respectively. The critical t at the 5 percent level
is approximately two.



market asset holdings have greater impact
on purchases, as in the estimates for all
consumer durables. The
, capital
cost, and lagged stock terms are all of the
expected sign and are usually significant.
The magnitudes of these coefficients are
also reasonable. The lagged versions of
the estimated model for the two sectors,
equations (27) and (30), do have a superior fit to the unlagged models, (26) and
(29); and the quarterly speed of adjustment implied by these lagged models is
over 12 percent for autos and parts, and
over 13 percent33 for nonauto consumer
durables; at annual rates these are both
over 40 percent.
It is interesting to note that the estimated debt and financial assets coefficients
are so much larger in the autos and parts
regressions than in the nonauto regressions,
in spite of the fact that autos and parts
make up not quite half of total consumer
durable purchases. The consumer's financial position seems to have more impact
on his decision to purchase an automobile
than it does on his decision to purchase a
household consumer durable.34 This is a
worthwhile subject for further research.
Standard regression equations for both
sectors where the debt and financial assets
variables have been excluded have been
estimated and appear as equations (28)
and (31) in Tables 1 and 2. For both sectors, regression equations which incorporate the results of the liquidity model are
superior to the standard regressions. They
have a better fit, a lower standard error,
and a smaller autocorrelation coefficient.
Furthermore, in the nonauto consumer
These adjustment rates assume a quarterly replacement rate of .07 for autos and parts and .045 for nonauto
consumer durables.
As a result of indivisibilities in the consumer's portfolio, the absolute size of the loss from selling a durable,
and not just the loss per unit of the durable, could be
important to consumer behavior. High priced durables
such as automobiles would have a greater potential
absolute loss from a forced sale than low priced durables,
and this might explain the result found above.


durables case the standard regression has
an impossibly low speed of adjustment;
only .5 percent of the discrepancy between desired and actual stocks is made up
within the quarter—an annual rate of 2
Disaggregation of the consumer durable
sector into its autos and parts and nonauto consumer durables components has
resulted in further tests of the liquidity
model. The results are still strongly supportive of this hypothesis.
III. Implications for Monetary Policy
and Concluding Remarks
The consumer durable expenditure model
which incorporates the results of the liquidity model developed in this paper leaves
monetary policy with a strong role to play
in the demand for one of the most volatile
components of gross national product.
Three routes for monetary policy effects on
consumer durable expenditures can now
be envisioned.
1) Monetary policy affects interest
rates and hence the user rental cost of
capital. Tight monetary policy which
raises interest rates will be a strong deterrent to consumer durable purchases because of the high interest elasticity of consumer durable demand indicated by empirical results in this paper.
2) In a Tobin, Foley-Sidrauski theoretical framework, monetary policy has a
strong influence on asset prices in the
economy. Tight monetary policy will lead
to a fall in stock and bond prices and will
thus result in a smaller valuation of the
gross financial assets in the community.
This will lead to decreased purchases of
durables because consumers' financial positions have deteriorated; they are now left
with a high probability of income shortfalls that would have to be met by the distress sale of consumer durables or a drop
in consumption.
3) Past monetary policy will have
affected the cost and availability of credit

VOL. 66 NO. 4


to the consumer and will have thus affected
the size of consumers' liabilities. Easy past
monetary policy which has encouraged the
buildup of consumer debt holdings will
eventually prove a deterrent to future consumer durable purchases. The increased
debt holdings force the consumer to desire more liquid assets.35
Viewing the consumer durable as an illiquid asset which must be traded in imperfect capital markets has led to a consumer durables demand model where perceived risk, and consumer liabilities and
gross financial asset holdings influence consumer durables expenditure. In contrast to
other work on macro-economic financial
asset effects where net wealth influences
consumer behavior,36 this approach finds
that the composition of the consumer balance sheet is critical to spending decisions.37 The empirical estimates of this
model have proved very encouraging, and
several new and apparently potent channels of monetary policy that affect aggregate demand have been proposed. Furthermore, a traditional path for monetary
policy effects on consumer durable expenditure has proved to be quite powerful
in the model estimated here.
The liquidity model developed in this
paper should also have applications in such
areas as residential housing demand, and
this will be the subject of further research.
Many producer's goods, such as inven35
Simulations with a macro-econometric model (see the
author) indicate that the two liquidity channels discussed in (2) and (3) far outweigh the interest rate effects
discussed in (1), and are indeed extremely important in
the determination of aggregate demand.
For example, see Modigliani.
An important implication of the analysis of this
paper is that changes in the composition of the household balance sheet which leave net wealth unchanged
can affect the expenditure behavior of households. An
increase in indebtedness matched by an increase in holdings of nonfinancial assets which leaves net wealth constant would still lead to a future decline in consumer
durable expenditure; a decrease in the value of financial
asset holdings matched by an increase in nonfinancial
asset holdings that left net wealth constant would also
lead to a decline in consumer durable demand.



tories and producer's durables are also illiquid assets; incorporating this feature
into investment models might throw light
on other possible channels of monetary
policy effects in our economy. This avenue
of monetary research should prove very
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