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FINANCIAL INDUSTRY
June 1991

Federal Reserve Bank of Dallas

The Texas Credit Crunch
Fact or Fiction?
Jeffery W. Gunther
Economist
and
Kenneth J. Robinson
Senior Economist

1988
1989
1990
This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)1991

Financial Industry Studies
Federal Reserve Bank of Dallas

June 1991

President and Chief Executive Officer
Robert D. McTeer, Jr.
First Vice President and Chief Operating Officer
Tony J. Salvaggio
Senior Vice President
George C. Cochran, III
Vice President
Genie D. Short
Senior Economist
Kenneth J. Robinson
Economist
Jeffery W. Gunther
Financial Analyst
Kelly Klemme

Financial Industry Studies is published by the Federal Reserve Bank
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do not necessarily reflect the position of the Federal Reserve Bank
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Subscriptions are available free of charge.
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The Texas
Credit Crunch:
Fact or Fiction?
Jeffery W. Gunther
Economist
and
Kenneth J. Robinson
Senior Economist
Financial Industry Studies Department
Federal Reserve Bank of Dallas

T

otal inflation-adjusted loans extended
by commercial banks in Texas peaked
at more than $107 billion in the first
quarter of 1986 and then declined 46
percent by the first quarter of 1990. The
sharp curtailment in lending activity at
Texas banks has given rise to increasing
concerns that economic activity in the
state is being squeezed by a lack of credit.
In this scenario, banks are alleged to be
unwilling or unable to extend loans with
which to finance worthwhile investment
projects. Banks may be unwilling to
extend new loans, either as part of a
retrenchment from overly aggressive
lending practices of the past or in response to increased regulatory scrutiny.
If bank capital falls below regulatory
standards, banks may be unable to extend
additional loans because of regulatory
restrictions. While these supply-side
effects may be at work in the decline
in lending activity at Texas banks, it is
also possible that demand-side factors
are playing a role. In particular, Texas
bankers may be facing a lack of creditworthy borrowers, in which case the
decline in lending would be an appropriate response.
Whether the Texas economy has
suffered from a credit crunch is a difficult
question to answer empirically. In fact,

economists often differ on the precise
meaning of the term credit crunch. The
approach taken here is to examine whether,
and to what extent, banking conditions
and economic activity in Texas are related.
We can expect that the deterioration in
economic activity precipitated by falling
oil prices adversely affected the banking
sector in Texas. Our interpretation of a
credit crunch centers on whether the
decline in the Texas banking sector exerted
a "feedback" effect on real economic
activity in the state. If so, it would be
evident that credit availability from Texas
banks, or the lack thereof, affected economic
activity in the state.
Our statistical tests indicate that economic activity had a strong influence on
banking-sector output. W e find little
evidence, however, that banking-sector
activity in Texas has affected the overall
economy of the state. Before examining
these results, we first offer some background on possible links between the
financial system and economic activity.
Then, the statistical techniques used are
described, and the results are interpreted.
Finally, some policy implications arising
from these results are offered.

Background
There has been a resurgence of interest
among economists regarding the link
between financial structure and the performance of the economy. The economic
upheaval of the Great Depression generated much interest in the role financial
factors play in influencing movements in
economic activity. However, the Keynesian
revolution that swept the economics
profession after the depression, while
recognizing the importance of financial
factors, examined the role of money in its
theory of liquidity preference, as opposed
to the broader measure of credit. Even so,
traditional Keynesians attached little
importance to money in explaining movements in real output. In contrast, the work
of Friedman and Schwartz, along with the
resurgence of monetarism, further empha1

sized the importance of money as the only
financial aggregate in macroeconomic
analysis.1
A movement toward a reconsideration
of the role of financial structure in influencing real economic activity began with the
work of Gurley and Shaw (1955), who
emphasized the broader measure of credit
as opposed to the more narrowly defined
money aggregate. Credit emerges as a key
variable behind movements in output,
especially in more advanced economies.
This expanded role of credit is due, in part,
to the existence of close money substitutes
in more advanced economies. In short,
what economists call the "transmission
mechanism" of monetary policy—or the
manner in which monetary policy affects
the real economy—is different in the
Gurley-Shaw hypothesis. Credit supply,
rather than the money supply, is the
principal channel of monetary policy.
In a related vein, Bernanke (1983)
stresses that credit contributed to the
economic collapse suffered during the
Great Depression. Building on the framework established by Friedman and
Schwartz, Bernanke argues that monetary
factors alone are insufficient to explain the
sharp decline in output during the depression. The financial shocks suffered during
the 1930s reduced the quality of financial
intermediation services offered. According
to Bernanke, the real service performed by
the banking system is the differentiation
between good borrowers and bad borrowers. Thus, the "cost of credit intermediation"—or the cost of channeling funds from
savers and lenders to good borrowers—is
increased when bank failures become
widespread, as in the Great Depression. As
evidence for the important role of credit,
Bernanke first estimates national output as
a function of only monetary variables over
the period 1919-41. He then shows that

1 Friedman and Schwartz (1963). See Gertler (1988) for
a summary of the role of financial factors in affecting
aggregate economic activity.

2

adding proxy variables for the general
financial crisis (including real deposits of
failed banks, liabilities of failed businesses,
and yield differentials between safe securities
and risky securities) significantly improves
the results over those from equations that
include only monetary variables.
Bernanke's evidence is consistent with
the proposition that the financial collapse
of the 1930s exerted a negative effect on
real economic activity, independent of any
effect arising from a decline in the money
supply. Evidence about the Canadian
experience during the Great Depression
suggests that a key element contributing to
a downturn in economic activity is bank
failure, rather than just financial-sector
weakness. Canada's branch-banking system
proved immune to runs and panics during
the early 1930s. The banking sector,
however, did shrink significantly in
Canada: loans and deposits declined, bank
stock prices dropped, and the number of
branches diminished. In an environment
where the banking sector was severely
weakened but where widespread failures
did not occur, Haubrich (1990) finds no
evidence that the cost of credit intermediation had a major impact on the course of
economic activity. In Canada, then, a
shrinkage of the banking system did not
significantly influence Canadian economic
activity, suggesting that without outright
failures, the cost of credit intermediation
has few macroeconomic effects. Failures
become important by increasing the real
cost of transferring funds from lenders
(savers) to borrowers (investors).
Bernanke and Gertler (1987) develop a
model that stresses the importance of
bank capital. The net worth of the banking system determines the amount of risky
projects financed by banks, which, in turn,
affects investment and output. They also
stress that monetary policy operates
primarily through its effects on bank
credit, in contrast to the traditional
Keynesian and monetarist interpretations.
Samolyk (1989) demonstrates how, given
geographically segmented banking

markets, local banking conditions affect
real investment and output in a particular
region. Again, bank capital is a critical
feature. Economic shocks to a particular
region, such as a sharp and unanticipated
decline in oil prices, can reduce bank
capital. The regional banking system can
then become capital-constrained, which
leads to underinvestment in risky projects.
The Texas banking and economic
environment of the past decade offers a
unique setting to investigate for the effects
of financial conditions on economic
activity. The regional economic shock
suffered in the Southwest and the associated effects on banks' capital positions
fulfill the general criteria of the Bernanke—
Gertler and Samolyk approaches. In Texas,
bank equity capital, adjusted for inflation,
fell 40 percent from its peak in the fourth
quarter of 1985 to the first quarter of 1990.
If there are no close substitutes for bank
loans, then the decline in bank capital and
the associated reduction in bank lending
may have adversely affected economic
activity in the state. The next section
describes the empirical methodology used
to analyze the effect of financial structure
in Texas on economic activity in the state.2

Cross Correlations
and Lead-Lag Relationships
Our approach focuses on certain
linkages implied by the various theories
that posit a link between the nonfinancial
and financial sectors of the economy.
Chart 1 shows the possible linkages
between the banking sector and economic
activity implied by the existence of a
credit crunch. A shock to real economic
activity can have a significant negative
effect on the overall health of the banking
system—either directly by affecting bank
earnings, and thus banking conditions
generally, or indirectly through a decline
in collateral values, which affects the
quality of bank loans and (ultimately)
bank profitability. This decline in the
financial condition of banks can affect
banks' ability to extend credit, which, in

Chart 1
Linkages Between Banking
and Economic Activity
Economic

Collateral
Values

Bank
Credit

Conditions

turn, may further influence economic
growth. Any causal connection between
bank performance and economic activity
is difficult to isolate, though, particularly
at the regional level. Even if banks in a
particular region are weak, it might be
possible to break or circumvent this
linkage if creditworthy borrowers are able
to negotiate loans with stronger financial
institutions, including nonbank financial
institutions, that are located outside the
region. Thus, a downturn in regional
banking conditions will not necessarily
have a significant independent effect on
economic activity that causes further
deterioration in a regional economy. But
the broader the market area that is affected by the financial deterioration, the
more ineffective this potential release
valve becomes.
A direct test of the effect of financial
structure on economic activity presents
formidable econometric difficulties. Chart 2
shows movements in a measure of banking
activity—bank credit, or total loans extended

2

Another view of the role of financial factors in

output fluctuations is found in the real business-cycle
framework. With this approach, financial structure is
largely irrelevant. See Plosser (1989) for a summary of
the related literature.

3

Chart 2
Bank Credit and Personal Income, 1978-90
(Adjusted for Inflation, 1978:1 = 100)
Loans

Income

S O U R C E S : Consolidated Reports of Condition and
Income; Federal Reserve Bank of Dallas
Research Department

by Texas banks, adjusted for inflation.3
Also included in Chart 2 is a measure of
economic activity, personal income in
Texas, also adjusted for inflation. These
two variables moved fairly closely together
in the first half of the decade but separated
significantly in subsequent years.
One measure of the degree of association
between two variables is the contemporaneous correlation coefficient. If two series
are positively and perfectly correlated, then
the contemporaneous correlation coefficient
is equal to 1. If there is no correlation
between two variables, their correlation
coefficient equals zero. The correlation
coefficient between the two series shown in
Chart 2 equals 0.54, indicating a fairly high
degree of association between real (inflationadjusted) loans and real income in Texas.
However, in attempting to discover what, if
any, effect the decline in lending by Texas
banks exeited on the Texas economy, w e

s The results reported here are for loans at domestic
offices of Texas banks. Qualitatively identical results
are obtained when loans at both domestic and foreign
offices of Texas banks are used.

4

must look behind the simple correlation
coefficient between the two series. This
measure tells us nothing about how the two
series might actually be related. It is possible
for two time series that are not related to
show a high spurious correlation if each
series is highly correlated with its own past
values. Moreover, the contemporaneous
correlation between two series sheds little
light on the direction of their association. In
our case, w e can expect a fairly strong
linkage running from economic activity to
banking conditions. What is of interest is
whether banking conditions then exert any
feedback effect on economic activity in
the state.
One approach to gauging the direction
of the association between two variables is
to estimate their temporal ordering. A
variety of methods have been used in this
regard, but the most intuitive is simply to
calculate the cross-correlation function
between "shocks" in the relevant variables.
The cross-correlation function includes not
only the contemporaneous correlation but
also the correlations between each variable
and lags of the other variable. Because the
cross-correlation function measures the
strength of a relationship between two
variables at different lags, it can indicate
the direction of any association. For
example, if current measures of banking
conditions exhibit large cross correlations
with lagged measures of economic activity,
then w e may say that economic conditions
would be a leading indicator of banking
activity or that economic conditions are a
predictor of banking conditions. Similarly,
a strong correlation between current
economic output and lagged measures of
banking-sector activity would indicate that
current levels of economic activity are
related to past values of bank performance.
If this is the case, some evidence exists that
banking-sector conditions in Texas affected
economic activity in the state, so there is a
feedback effect from banking to economic
activity. In addition, by examining the
cross-correlation function of shocks in the
time series, as opposed to the variables

Chart 3
Relationship Between Oil Prices
and Real Personal Income
.30

oil prices lagged eight quarters is significant. Note that negative correlations are
not expected between oil prices and real
income and that the lagged correlations
that are negative are not statistically
significant. If w e wish to detect the direction of the relationship between oil prices
and real income, w e need to know
whether past values of oil prices, as a
group, are significantly correlated with
current real income in Texas. The test
statistic used to evaluate the statistical
significance of the cross correlations as a
group is the so-called Q statistic.5

Confidence Boundary

7

6

5

4

3

2

1

Lag of Real Oil Prices

themselves, the spurious correlation that
can arise between two variables is avoided.
For additional discussion on the techniques
used to estimate the cross-correlation
functions, see the Appendix. 4
W e used the following variables in our
analysis of the effect of credit availability on
economic activity in Texas: real oil prices;
real loans extended by Texas commercial
banks; and two different measures of
economic activity, real personal income in
Texas and Texas nonagricultural employment. Our time period runs from the first
quarter of 1978 to the first quarter of 1990.
W e anticipate positive correlations between
our variables. Oil prices and measures of
economic activity should move together, as
should economic activity and lending
activity at Texas banks.
The first cross correlations calculated are
those between past movements in oil
prices and real personal income in Texas,
as shown in Chart 3. The dotted lines in
the chart indicate the approximate 95percent confidence interval. That is, if a
particular correlation coefficient lies within
these lines, it is not different from zero in a
statistical sense. From Chart 3, the correlation of oil prices lagged two quarters with
current real income is almost significantly
different from zero, and the correlation for

W e examine eight lagged correlations,
implying that after two years, the cross
correlations between the series are insignificant. When the cross correlations
between lagged values of oil prices and

4

Cross correlations are used mainly for pedagogical

purposes to highlight more easily the concepts
involved in our approach. More formal statistical
techniques exist to detect lead-lag relationships, or
what economists call Granger causality. Gunther,
Lown, and Robinson (1991) estimate a four-variable
vector autoregressive model of the Texas economy to
examine the issue of feedback effects from banking to
economic activity. Their model has the following
variables: oil prices; various measures of U.S. economic activity; various measures of Texas bankingsector activity—namely, loans and equity; and various
measures of economic activity in Texas. Preliminary
calculations of the impulse response functions and
variance decompositions indicate the same qualitative
results obtained in the analysis here. There is some
limited evidence, though, that shocks to bank equity
may play a small role in explaining movements in
economic activity in Texas.
5

The Q statistic is defined as
K

Q ( K ) = n(n + 2 ) Y

1

rA, (k),

where rx is the sample cross-correlation coefficient,
k represents the lag of the cross correlation, n represents the number of data points used in the calculation of rxy' and the chosen value of K is such that it
,
can be assumed that the r xv (k)'s for k > K are negligible. This test statistic is approximately distributed as
chi-square with K degrees of freedom. For a layman's
guide to the techniques used here, see Vandaele
(1983), chap. 11.

5

current economic activity—as measured by
real income—are calculated, the value of
the Q statistic equals 27.24. This indicates
that, as a group, the first eight cross
correlations of lagged values of oil with
current income are significantly different
from zero. Thus, as expected, oil prices can
be considered a leading indicator of
economic activity (as measured by real
income) in Texas.
W e next compare real income and real
loans. The cross correlations of real loans
and lagged personal income are found in
Chart 4. Again, w e expect lagged personal
income to be positively correlated with
bank lending, and the negative correlations
in Chart 4 are not statistically significant.
When the cross correlations between
lagged values of real personal income in
Texas and current real loans are calculated,
the eight lagged cross correlations, as a
group, are significantly different from zero.
The Q statistic equals 19.65. However, the
correlations of lagged values of bank
lending with current economic activity are
not significantly different from zero, as
indicated by a Q statistic of 6.36. Chart 5
shows these cross correlations. A summary
of these findings appears in Table 1.
Cross correlations calculated using Texas
nonagricultural employment in place of

Chart 5
Relationship Between Bank Loans
and Real Personal Income

Confidence Boundary
.23

-.29 - •

Confidence Boundary
7

6

5

4

3

2

1

Lag of Real Bank Loans

real personal income are shown in Charts 6
through 8. When employment is used as a
measure of economic activity, the same
qualitative results are obtained. A summary
of the significance of these cross correlations appears in Table 2. These results
suggest that economic conditions in Texas
can be viewed as a leading indicator of
banking conditions in the state. There is no
evidence, however, that lending by Texas
banks exerted any feedback effect on
economic activity.

Interpretation and Policy Implications
Chart 4
Relationship Between Personal Income
and Real Bank Loans

.29 •

•30

Confidence Boundaiy

.29

.17

.16

-.04

1
-.12

-.29 -

-.15

-.22
Confidence Boundary
7

6

5

4

3

2

Lag of Real Personal Income

6

1

Our results suggest that the upheaval
suffered in the Texas banking sector had
little effect on overall economic activity in
the state. While w e find evidence that
economic events affected the banking
sector, we can find little evidence that
lending activity by Texas banks exerted
any influence on overall economic activity.
One possible explanation for these results
is that capital apparently Hows fairly well
across regions. If Texas banks were either
unable or unwilling to extend viable loans,
then perhaps banks and other financial
institutions outside the state fulfilled this
function.
It has also been argued that banks are
becoming less important in financing

Table 1
Significance of Cross Correlations Using T e x a s Real Personal Income

Variable

Q statistic

O I L => R P I C T X

27.24*

R P I C T X => R L O A N S

19.65*

R L O A N S => R P I C T X

6.36

Definitions of variables
OIL = inflation-adjusted oil prices.
RPICTX = real personal income in Texas.
RLOANS = real loans extended by Texas commercial banks.
* Statistical significance at the 5-percent level.

economic activity as corporations increasingly make use of more direct financial
intermediation through credit market
instruments, such as commercial paper.
Savers are also finding it easier and, presumably, more attractive to bypass banks, as
witnessed by the strong growth of money
market mutual funds over the past decade.
Hence, the unique role of bank lending in
affecting output may have been reduced.

money markets in the form of commercial
paper. At the same time, information costs
regarding lender quality would increase,
making it more difficult for financial institutions outside a particular region to extend
credit to small enterprises. It could easily be
the case that many small businesses were
adversely affected by the decline in lending
activity at Texas banks, but this effect was
obscured by aggregate state data.

A trend away from the more traditional
use of banks in the financial intermediation
process would not appear to be as attractive
an option for small businesses, however.
Smaller entities lack access to national

Finally, it must be acknowledged that the
swift interventions and resolutions by the
various regulatory agencies, while not
without serious unintended consequences,
were successful in averting any widespread

Table 2
Significance of Cross Correlations Using T e x a s Nonagricultural E m p l o y m e n t

Variable

Q statistic

O I L => E M P L O Y M E N T

17.61*

EMPLOYMENT

RLOANS

R L O A N S => E M P L O Y M E N T

13.56t
6.43

Definitions of variables
OIL = inflation-adjusted oil prices.
EMPLOYMENT = total Texas nonagricultural employment.
RLOANS = real loans extended by Texas commercial banks.
* Statistical significance at the 5-percent level,
t Statistical significance at the 10-percent level.

Chart 7
Relationship Between Employment
and Real Bank Loans

Chart 6
Relationship Between Oil Prices
and Employment

7

6

5

4

3

2

1

0

Lag of Real Oil Prices

financial panic in the state—unlike what
occurred during the financial collapse in the
Great Depression. These actions also could
explain the lack of any effect of bank credit
availability on economic activity.
Despite the state focus of the analysis
here, the problems examined have become
national in scope. Increasing numbers of
bank failures nationally have given rise to
heightened fears of the impact of a decline
in bank services on economic activity.
While a widespread banking collapse of
the type suffered during the 1930s would
likely have serious consequences for
economic activity, our results suggest that
concerns about the impact of credit
availability resulting from a regional banking
decline may be somewhat unwarranted,
given current institutional arrangements.

Lag of Employment

integrated capital markets. Also, increased
consideration should be given to removing
the regulatory restrictions on geographical
expansion under which banks currently
operate. Financial markets have become
increasingly integrated on a national and
international basis. Banks, however, are still
prevented from establishing national networks of branches.6 Greater diversification in
bank lending, facilitated by nationwide
banking, would reduce the cost of intermediation services provided by banks.

Chart 8
Relationship Between Bank Loans
and Employment

Improvements in the processing and
transmitting of information have lowered the
cost of nonbank credit relative to bank
credit. As a result, consolidation and shrinkage appear to be appropriate bank responses
in light of technological advancement and
the heightened competition in increasingly

6

The recent proposal of the U.S. Department of the

Treasury (1991) for restructuring the banking system
advocates a move toward interstate branching.

8

Lag of Real Bank Loans

Appendix
Cross-Correlation Functions
The degree of association between
two variables at different time periods can
be estimated using the cross-correlation
function. The cross correlations between
two series, x and y, for different time
periods are defined as
r

E[(x»-/ix)(y^-^)]tk
C7x(Ty

= 0>±1>±2

where E is the expected-value operator
and where o x and a y are the standard
deviations and (ix and n are the means of
the stationary series x and y, respectively. (The other expressions are defined
in text footnote 5.) The cross correlations
need not be symmetric about k = 0. That
is, r xy (k) is not equal to r x y (-k). If a particular variable—say, x t —is a leading indicator of another variable, yt> then xt is strongly
correlated with future values of y t and is
not correlated with lagged values of y t . It
is in this sense that cross correlations
measure not only the strength of a relationship between two variables but also
the direction.
The first step in calculating the cross
correlations between two variables is to
ensure that both series are stationary.
When a series is highly correlated with its
own past values, which is a common occurrence with economic data, then that
time series is said to be autocorrelated.
When a particular series is highly autocorrelated, the cross correlations can be
very misleading. It is possible for two time
series that are not related to show a high
spurious correlation if each series is autocorrelated. As a result, the series must first
be filtered, or "prewhitened," before calculating the cross correlations. This procedure entails obtaining the correct time
series representation of each series, or
what is known as the appropriate ARIMA

model for each variable, and then crosscorrelating the white-noise residuals. Intuitively, if we want to judge whether one
variable—say, x t —can explain the behavior of another variable, y t , we should first
eliminate all variation in y t that can be
explained by past movements in y t .
We fitted time series models to each
of the variables used in our analysis: inflation-adjusted oil prices (OIL), real
personal income in Texas (RPICTX), total
Texas nonagricultural employment (EMPLOYMENT), and real loans extended
by Texas commercial banks (RLOANS). 1
Each series needed to be differenced
once in logarithms to obtain stationarity.
After differencing, OIL was determined
to be white noise, obviating the necessity
of fitting an ARIMA model. 2 Investigation
of the remaining series indicated that
R P I C T X is best r e p r e s e n t e d by an
ARIMA(2,1,0) model, and EMPLOYMENT
by an ARIMA(4,1,0) model; R L O A N S
appeared to be well-represented by an
ARIMA(1,1,0) model. Thus, the first difference of OIL and the residuals from these
three ARIMA models were the variables
used in the cross-correlation calculations.

OIL is defined as the price of West Texas
Intermediate crude divided by the consumer price
index. RPICTX is personal income in Texas
divided by the consumer price index. RLOANS is
total loans at domestic offices of Texas banks,
deflated by the consumer price index. The
consumer price index is the index for all urban
consumers and is obtained from the Citibase data
bank, as is the price of West Texas Intermediate
crude. Personal income in Texas and EMPLOYMENT are from the Federal Reserve Bank of
Dallas Research Department, while total loans
are from the Consolidated Reports of Condition
and Income.
1

The Box-Ljung test statistic for white noise for
the first difference of OIL, at 18 lags, was 9.92.
2

9

References
Bernanke, Ben S. (1983), "Nonmonetary
Effects of the Financial Crisis in the Propagation of the Great Depression," American
Economic Review 73 (June): 257-76.
, and Mark Gertler (1987), "Banking
and Macroeconomic Equilibrium," in New
Approaches to Monetary Economics, ed.
William A. Barnett and Kenneth Singleton
(New York: Cambridge University Press),
89-111.
Friedman, Milton, and Anna Jacobson
Schwartz (1963), A Monetary History of the
United States, 1867-I960 (Princeton:
Princeton University Press for National
Bureau of Economic Research).
Gertler, Mark (1988), "Financial Structure
and Aggregate Economic Activity: An
Overview," Journal of Money, Credit, and
Banking 20 (August, pt. 2): 559-88.
Gunther, Jeffery W., Cara S. Lown, and
Kenneth J. Robinson (1991), "Bank Credit
and Economic Activity: Evidence from
Texas," Federal Reserve Bank of Dallas
Financial Industry Studies Working Paper
(Dallas, forthcoming).

10

Gurley, John G., and E.S. Shaw (1955),
"Financial Aspects of Economic Development," American Economic Review 45
(September): 515-38.
Haubrich, Joseph G. (1990), "Nonmonetary
Effects of Financial Crises: Lessons from the
Great Depression in Canada," Journal of
Monetary Economics 25 (March): 223-52.
Plosser, Charles I. (1989), "Understanding
Real Business Cycles," Journal of Economic
Perspectives 3 (Summer): 51-77.
Samolyk, Katherine A. (1989), "The Role of
Banks in Influencing Regional Flows of
Funds," Federal Reserve Bank of Cleveland
Working Paper No. 8914 (Cleveland,
November).
U.S. Department of the Treasury (1991),
Modernizing the Financial System: Recommendations for Safer, More Competitive
Banks (Washington, D.C.: U.S. Department
of the Treasury, February).
Vandaele, Walter (1983), Applied Time
Series and Box-Jenkins Models (New York:
Academic Press).