linkages between the financial and real sectors: an...
TRANSCRIPT
Linkages Between the Financial and Real Sectors:
An Overview
Simon Gilchrist∗ Egon Zakrajsek†
September 24, 2008
Prepared for the Academic Consultants Meeting, “Financial Stability and Linkages Between Financialand Real Sectors,” Federal Reserve Board, October 3, 2008. We thank Dan Sichel for helpful conversations.
∗Department of Economics Boston University and NBER. E-mail: [email protected]†Division of Monetary Affairs, Federal Reserve Board. E-mail: [email protected]
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1 Introduction
The United States is currently in the throes of an acute liquidity and credit crunch, by all
accounts, the severest financial crisis since the Great Depression. The roots of this crisis
lie in the collapse of the subprime mortgage market in the wake of an unprecedented and
unexpected fall in house prices. The financial turmoil subsequently spread to a variety of
other asset markets, causing massive liquidity problems in interbank funding markets, the
sudden collapse of several major financial institutions, and a sharp reduction in lending
activity; see Brunnermeier [2008] for a detailed account of the 2007–08 financial crisis.
In the hope of preventing the financial meltdown from engulfing the real economy, the
government in recent days announced plans to purchase a broad range of mortgage-related
assets from a variety financial institutions, an intervention in the global financial markets
at an unprecedented scale.
In this essay, we assess the likely implications of such financial disruptions for the real
economy. We begin the analysis with a brief snapshot of the current state of affairs and
compare current trends in economic activity with those experienced during the last two re-
cessions. We then discuss various linkages between the financial sector and the real economy
and identify three main channels by which disruptions in financial markets can influence
real activity: a pullback in spending owing to reductions in wealth; balance sheet mech-
anisms that lead to a widening of credit spreads, which curtail the ability of households
and businesses to obtain credit; and the direct effect of impairments in the ability of finan-
cial institutions to intermediate credit. Although these channels are well-understood from
a theoretical perspective, assessing their quantitative implications remains a considerable
challenge for macroeconomists. For example, a fall in output that follows a drop in lending
associated with a major financial disruption reflects both supply and demand considera-
tions. In addition, in a world with a rapidly changing financial landscape, it is difficult to
gauge the extent to which various financial asset market indicators provide consistent and
credible information about the relationship between the health of the financial system and
economic activity.
Our analysis consists of two parts. First, we review the recent empirical evidence on
the relationship between corporate credit spreads—the difference in yields between vari-
ous corporate debt instruments and Treasury securities of comparable maturity—and eco-
nomic activity, a link elucidated by the theoretical literature that emphasizes movements in
default-risk indicators as an important signal of disruptions in financial markets. We extend
the standard analysis by attempting to separate empirically the portion of the predictive
content of default-risk indicators for economic activity that reflects the usual countercyclical
movements in expected defaults from the part due to cyclical changes in the relationship
1
between measures of expected default risk and credit spreads. According to our results,
most of the information content of corporate credit spreads can be attributed to deviations
in the pricing of corporate securities relative to the expected default risk of their issuer, a
finding that suggests that signals about impending financial disruptions embedded in prices
of corporate debt instruments may account for a significant portion of the forecasting power
of credit spreads for economic activity.
To provide further insight into the linkages between the financial sector and the real
economy, we then discuss recent work that seeks to incorporate financial market frictions
into otherwise standard dynamic stochastic general equilibrium (DSGE) models. The aim
of this vein of research is to disentangle movements in the supply and demand for credit by
imposing a structural framework on macroeconomic data. In particular, using quarterly U.S.
data over the 1985:Q1–2008:Q2 period, we estimate a DSGE model based on the financial
accelerator framework developed by Bernanke, Gertler, and Gilchrist [1999].1 Although
the estimated model is relatively simple compared with other work in this area, the results
nonetheless provide a considerable insight into the importance of financial factors in business
cycle fluctuations. In particular, the model estimates suggest that financial disruptions are
responsible for sharp declines in output growth during the last two recessions and that
the easing of financial conditions during the second half of the last decade contributed
importantly to the investment boom of the late 1990s. In addition, the model estimates
imply that the current financial crisis—through its impact on business fixed investment—
appears to be responsible for a considerable portion of the observed slowdown in economic
activity.
2 Current Economic Conditions
In this section, we offer a brief assessment of recent economic developments. To do so,
we compare the time-series evolution of key macroeconomic variables during the current
episode to their evolution during the last two NBER-dated recessions. Figure 1 considers
the behavior of real GDP and its major components; Figure 2 examines the evolution of
monthly indicators of labor market conditions and economic activity; and Figure 3 focuses
on the housing sector. All series in the three figures are plotted as indexes that equal 100
during the quarter (month) that marks the beginning of the recession as dated by the
NBER. For the current episode, all quarterly (monthly) series are benchmarked so that
they equal 100 in 2007:Q2 (October 2007).
This past summer marked a one-year anniversary of the current financial crisis. Accord-
1Other formulations of financial market frictions in general equilibrium models include, for example,Fuerst [1995], Carlstrom and Fuerst [1997], Kiyotaki and Moore [1997], and Cooley, Marimon, and Quadrini[2004].
2
Figure 1: Real GDP and Its Selected Components
-16 -12 -8 -4 0 4 8
90
95
100
105Index peak = 100
Quarters to or from peak
1990:Q32001:Q12007:Q4
Quarterly
Real GDP
-16 -12 -8 -4 0 4 8
85
90
95
100
105
110Index peak = 100
Quarters to or from peak
1990:Q32001:Q12007:Q4
Quarterly
Consumption
-16 -12 -8 -4 0 4 8
75
80
85
90
95
100
105Index peak = 100
Quarters to or from peak
1990:Q32001:Q12007:Q4
Quarterly
Business fixed investment
-16 -12 -8 -4 0 4 8
90
100
110
120
130
140
150Index peak = 100
Quarters to or from peak
1990:Q32001:Q12007:Q4
Quarterly
Residential investment
Note: The panels of the figure depict the behavior of real GDP, real personal consumption, realbusiness fixed investment, and real residential investment at and around cyclical peaks as dated by theNBER. The black and blue lines are indexed to equal 100 during the quarter marking the beginning ofthe NBER-dated recession. The red lines—the current episode—are indexed to equal 100 in 2007:Q4.
ing to Figure 1, however, there is very little evidence that this turmoil has had a significant
negative effect on aggregate demand, as evidenced by the behavior of real GDP, consump-
tion, and business fixed investment. In contrast, output and consumption spending fell
markedly at the onset of the 1990–91 recession, and business fixed investment peaked sev-
3
eral quarters prior to the 1990:Q3 cyclical peak. Business expenditures on fixed capital
also dropped sharply several quarters prior to the onset of the 2001 recession, though the
deceleration in output and consumption was substantially less severe in 2001 relative to
that experienced during the 1990–91 recession. The cyclical downturn in 2001 was driven
in large part by the collapse of investment in high-tech equipment that followed the burst-
ing of the “tech bubble.” The recession of the early 1990s, in contrast, was considerably
more broader-based, and the slowdown in economic growth reflected, in part, the effects of
“financial headwinds” that impinged on consumer spending. A major difference between
the current episode and the previous two recessions can be seen in the behavior of residen-
tial investment, a component of aggregate demand that peaked in 2005:Q4 and has fallen
60 percent over the subsequent 10 quarters.
Figure 2 examines the evolution of labor market conditions and high-frequency indica-
tors of production and spending. Compared with the GDP and its selected components, the
dynamics of labor market indicators (the unemployment rate and private payroll employ-
ment) in the current situation match fairly closely the changes in labor market conditions
during the previous two recessions. Similarly, output in the factory sector—as measured
by the index of manufacturing industrial production—is beginning to show declines that
appear consistent with those of the past two cyclical downturns. By contrast, growth of
retail sales has remained relatively robust throughout the current period, though consumer
spending has shown some signs of weakness in recent months.
Figure 3 takes a look at the housing sector. As shown in the top left panel, (nominal)
house prices, as measured by the OFHEO purchase-only index, expanded briskly through
2005 before slowing noticeably in 2006. House prices were essentially flat through 2007, and
the first half of 2008 saw an outright decline in the OFHEO house price index. Consistent
with the deceleration in house prices, housing starts and sales of both new and existing
homes have plummeted from their peak levels reached early in 2006—housing starts have
dropped a whopping 100 percent, and home sales have fallen nearly 60 percent. This weak-
ness in the housing sector is also reflected in the massive buildup of new-home inventories,
as evidenced by the run-up in the months’ supply of new homes, which surged almost
60 percent over this period. In contrast, activity in the housing sector showed no signs of
slowing during the 2001 recession, and the downturn of the early 1990s was characterized
by a relatively modest slowdown in housing starts and a minor inventory buildup.
In summary, the seeds of the current economic slowdown can be traced to the sharp
slowdown in house price appreciation and the drop in residential investment that has oc-
curred since early 2006. Somewhat remarkably, both housing starts and home sales have
remained relatively stable—albeit at very weak levels—in recent months despite the tight-
ening of conditions in mortgage credit markets. Similarly, the growth of real GDP, business
4
Figure 2: Labor Market, Production, and Spending Indicators
-48 -40 -32 -24 -16 -8 0 8 16 24
90
100
110
120
130
140
150Index peak = 100
Months to or from peak
July 1990Mar. 2001Oct. 2007
Monthly
Unemployment rate
-48 -40 -32 -24 -16 -8 0 8 16 24
92
94
96
98
100
102Index peak = 100
Months to or from peak
July 1990Mar. 2001Oct. 2007
Monthly
Private payroll employment
-48 -40 -32 -24 -16 -8 0 8 16 24
85
90
95
100
105Index peak = 100
Months to or from peak
July 1990Mar. 2001Oct. 2007
Monthly
Manufacturing industrial production
-48 -40 -32 -24 -16 -8 0 8 16 24
80
85
90
95
100
105
110Index peak = 100
Months to or from peak*Control category.
Mar. 2001Oct. 2007
Monthly
Retail sales*
Note: The panels of the figure depict the behavior of the civilian unemployment rate, nonfarmprivate payroll employment, manufacturing industrial production, and real retail sales (control category)at and around cyclical peaks as dated by the NBER. The black and blue lines are indexed to equal 100during the quarter marking the beginning of the NBER-dated recession. The red lines—the currentepisode—are indexed to equal 100 in October 2007.
fixed investment, and consumption has been relatively well maintained during this period
of prolonged financial turmoil. In contrast, the sharp deterioration of labor market condi-
tions, along with the recent contraction in industrial output, provide compelling evidence
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Figure 3: Housing Sector
-16 -12 -8 -4 0 4 8
80
90
100
110
120Index peak = 100
Quarters to or from peak
1990:Q32001:Q12007:Q4
Quarterly
OFHEO house price index
-48 -40 -32 -24 -16 -8 0 8 16 24
100
150
200
250Index peak = 100
Months to or from peak
July 1990Mar. 2001Oct. 2007
Monthly
Housing starts
-48 -40 -32 -24 -16 -8 0 8 16 24
80
100
120
140
160Index peak = 100
Months to or from peak*New and existing homes.
July 1990Mar. 2001Oct. 2007
Monthly
Home sales*
-48 -40 -32 -24 -16 -8 0 8 16 24
40
60
80
100
120
140Index peak = 100
Months to or from peak
July 1990Mar. 2001Oct. 2007
Monthly
Months supply of new homes
Note: The panels of the figure depict the behavior of the OFHEO purchase-only house price index,housing starts; home sales (new and existing homes), months’ supply of new homes at and around cyclicalpeaks as dated by the NBER. The black and blue lines are indexed to equal 100 during the quarter(month) marking the beginning of the NBER-dated recession. The red lines—the current episode—areindexed to equal 100 in 2007:Q4 (October 2007).
that economic growth is stalling. Moreover, these indicators suggest that the current de-
celeration in economic activity is comparable—both in its timing and magnitude—to the
slowdowns that occurred during the previous two recessions.
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3 Finance and the Real Economy
The benchmark macroeconomic model used to study the behavior of firms and households
is predicated on the assumption that the composition of agents’ balance sheets has no
effect on their optimal decisions. Within this Modigliani-Miller paradigm, households make
consumption decisions based solely on permanent income—the sum of their financial wealth
and the per-period income obtained from the present discounted value of future wages.
Movements in financial asset prices shape agents’ spending decisions to the extent that
they influence households’ financial wealth, whereas changes in interest rates affect spending
decisions because they alter the present discounted values and hence reflect appropriately
calculated user-costs for financing real consumption expenditures. On the business side,
firms make investment decisions by comparing the expected marginal profitability of new
investment projects with the appropriately calculated after-tax user-cost of capital. The
relevant interest rate used in such calculations reflects the maturity-adjusted risk-free rate
of return appropriate to discount the future cash flows.
Financial market imperfections—owing to asymmetric information or moral hazard on
the part of borrowers vis-a-vis lenders—provide a theoretical link between the agents’ finan-
cial health and the amount of borrowing and hence economic activity in which they are able
to engage. Although models differ on details, contracts between borrowers and lenders gen-
erally require that borrowers post collateral or maintain some stake in the project in order
to mitigate the contracting problems associated with such financial market imperfections.
For example, when the borrower’s net worth is low relative to the amount borrowed, the
borrower has a greater incentive to default on the loan. Lenders recognize these incentive
problems and, consequently, demand a premium to provide the necessary external funds.
In general, this external finance premium is increasing in the amount borrowed relative to
the borrower’s net worth. Because net worth is determined by the value of assets in place,
declines in asset values during economic downturns result in a deterioration of borrowers’
balance sheets and a rise in the premiums charged on the various forms of external finance.
The increases in external finance premiums, in turn, lead to further cuts in spending and
production. The resulting reduction in economic activity causes asset values to fall further
and amplifies the economic downturn—the so-called financial accelerator mechanism.
Although the theoretical impact of changes in financial conditions on household and
business spending decisions through the financial accelerator mechanism is well understood,
quantifying the overall strength of this mechanism remains a challenge for macroeconomists.
This task is complicated by the fact that it is very difficult to distinguish the effect of a
slowdown in economic activity on household and firm spending owing to the usual demand
channels absent financial market frictions from the effect that such a slowdown may have
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through the financial accelerator itself. Nonetheless, a careful assessment of the empirical
implications of models that allow for financial frictions relative to those that assume per-
fect capital markets have allowed researchers to make substantial progress in assessing the
empirical relevance of changes in financial conditions for real activity.
On the household side, the permanent income model of consumption has stark impli-
cations for the responsiveness of consumption to both income and asset values. Transitory
changes in income have very little effect on permanent income and hence consumption.
Reasonably calibrated versions of such models imply that households are relatively insensi-
tive to changes in asset values, suggesting that households should increase consumption by
three to four cents for every dollar increase in their financial wealth. More importantly, to
a first approximation, the value of housing does not represent net wealth for the household
sector, because an increase in house values is also an increase in the implicit rental cost of
housing. As a result, the household sector is no better or worse off when housing values
rise; see Buiter [2008] for a thorough discussion.
Empirical research provides compelling evidence against the permanent income model
of consumption in favor of models in which the quality of household balance sheets plays an
important role in determining their consumption decisions. A variety of studies shows that
household consumption is excessively sensitive to movements in transitory income. Whereas
the exact cause of this excess sensitivity is subject to a considerable debate, the excess sensi-
tivity is generally attributed to the fact that at least a subset of households faces significant
borrowing constraints or engages in precautionary-savings behavior because of imperfect
insurance. Similarly, in contrast to the predictions of the permanent income model, both
microeconomic and macroeconomic studies suggest an important link between house prices
and household consumption (see, for example, Case, Quigley, and Shiller [2005]; Carroll, Ot-
suka, and Slacalek [2006]; and Campbell and Cocco [2008]). Estimates of the housing wealth
effect vary but generally imply that household consumption increases by an amount ranging
from 3 to 10 cents for every dollar increase in housing wealth. This response is generally
attributed to the fact that at higher equity levels, households can obtain larger home mort-
gage loans and thus maintain high consumption levels while financing a home. Similarly,
existing home owners may engage in mortgage equity withdrawals to finance high levels of
consumption relative to their income. Although the estimated sensitivity of consumption
to housing values appears small, the significant decline in U.S. house prices experienced
during the last two years would imply a substantial drag on household consumption.
Empirical research also provides evidence that supports the notion that corporate bal-
ance sheets influence investment spending, though this evidence is more contentious. It is
well known that business investment spending is strongly correlated with corporate cash
flow. Earlier research, initiated by Fazzari, Hubbard, and Petersen [1988], has argued that
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cash flows stimulate investment because internal funds are a cheaper source of finance than
external funds. Critics, however, point out that current cash flows may also provide signals
about future profits, which, in turn, determine the firm’s net worth and hence the strength
of its balance sheet. That said, the available evidence suggests that the cash flow mechanism
is quite strong for smaller firms, firms with a limited access to corporate credit and equity
markets, or firms with weak balance sheets (see, for example, Gilchrist and Himmelberg
[1995]). Some of more recent research has questioned the macroeconomic relevance of this
effect by arguing that for large firms that account for the bulk of investment spending,
current cash flows serve mainly as signals about future profit opportunities rather than
indicators of the strength of their balance sheets (see, for example, Cummins, Hassett, and
Oliner [2006] and Rebelo, Eberly, and Vincent [2008]). Nonetheless, studies that analyze
investment spending during financial crises show that large negative shocks to firms’ bal-
ance sheets can have important adverse consequences for the investment decisions of large
firms, at least during periods of acute financial distress (see, for example, Aguiar [2005] and
Gilchrist and Sim [2007]).
According to the currently available data, corporate balance sheets appear to be in
relatively good conditions. The amount of liquid assets on the balance sheets of nonfinancial
firms is high by historical standards, and corporate profits have been surprisingly well
maintained in light of the persistent strains in financial markets. At the same time, credit
spreads on a wide variety of corporate debt instruments have widened significantly since
the middle of last year, a development that is consistent with a deterioration in the overall
financial condition of the corporate sector or a worsening of conditions within the financial
sector that serves as an originator and guarantor of corporate debt instruments. Although
macroeconomic evidence offers a mixed guidance on the importance of interest rates for
investment spending, recent work by Gilchrist and Zakrajsek [2007] using firm-level data
shows that investment is highly responsive to changes in corporate credit spreads. Thus,
although the corporate sector has maintained relatively strong balance sheets, it is still the
case that rising credit spreads may be reducing current investment spending. (We return
to this issue below.)
The financial mechanism linking balance sheet conditions of borrowers to real activity is
often described as the “broad credit channel.” Financial institutions are also likely to suffer
from asymmetric information and moral hazard problems when raising funds to finance
their lending activities. The focus of this so-called “narrow credit channel” is the health of
financial intermediaries and its impact on the ability of financial institution to extend credit.
In a fractional reserve banking system, deposits provide a source of funds for lending with
only a small fraction of total deposits held as reserves. Because a tightening of monetary
policy drains reserves from the banking system, poorly capitalized banks that are unable
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to raise external funds cut back on their lending. Bank-dependent borrowers, in particular
small firms and households that have few alternative sources of credit, reduce spending.
Kashyap and Stein [2000] document the empirical validity of this mechanism by showing
that small U.S. commercial banks that are poorly capitalized are especially sensitive to
changes in the stance of monetary policy. Although this bank lending channel appears to
have important effects on the lending behavior of smaller banks, such banks account for only
a small fraction of total bank lending in the United States, which suggests that the bank
lending channel may not be a quantitatively important channel through which monetary
policy affects the real economy. In a recent paper, however, Cetorelli and Goldberg [2008]
argue that this lending channel may also be at work at large commercial banks operating
primarily in domestic markets. In contrast, commercial banks with global operations are
able to offset declines in domestic deposits through internal funds obtained from their
global subsidiaries. In times of a worldwide financial distress, however, the ability of global
subsidiaries to provide internal funds to U.S. financial institutions is also likely to be limited
in scope, a development that would further strengthen the bank lending channel.
Although monetary policy may not have a large direct impact through the bank-lending
channel, reductions in bank capital during economic downturns can also reduce lending
activity. As economic activity slows and defaults rise, the quality of bank loan portfo-
lios deteriorates. Banks seeking to shore up their capital or to meet regulatory capital
requirements tighten their credit standards and cut back on lending, an inward shift in
loan supply that curtails spending of bank-dependent borrowers (see, for example, Van den
Heuvel [2007].) The strength of this mechanism depends on the overall health of the bank-
ing sector and on the extent to which firms and households are bank dependent. In the
United States, the bulk of investment spending is financed by relatively large firms that
rely primarily on corporate bond and equity markets to finance their capital expenditures.
Nonetheless, certain corporate debt instruments—most notably commercial paper—are typ-
ically backed by lines of credit at commercial banks. In addition, a substantial portion of
business financing through commercial and industrial loans relies on such credit lines. In
times of financial turmoil, even large nonfinancial firms may have a difficult time raising
capital in arms-length markets. As these firms tap their backup lines of credit to finance
inventories or operating expenditures in the face of falling revenues, banks may be forced
to make further cuts in lending to bank-dependent borrowers.
In summary, the recent drop in house prices is likely to bring about a reduction in
consumption spending through its impact on household borrowing and mortgage equity
withdrawals. In addition, the usual wealth channel implies that recent declines in stock
prices may also reduce household consumption, though empirical estimates suggest this
effect is likely to be relatively modest. Because nonfinancial corporate balance sheets remain
10
relatively strong, investment spending is likely to remain relatively solid but will slow to the
extent that corporate borrowing rates remain elevated because of persistent and intensifying
strains in financial markets.
The direct effect of falling values of assets held by the financial sector is more difficult
to assess. Although there is clear evidence that reductions in bank capital have important
implications for the lending behavior of small banks, there is less direct evidence to support
the claim that a capital channel has important implications for the lending behavior of
large banks and nonbank financial intermediaries. Nonetheless, a sharp pullback in lending
by large commercial banks and nonbank financial institutions during the current financial
crisis—owing to lack of liquidity in the interbank funding markets or a retrenchment in
lending as these institutions seek to replenish depleted capital—would likely cause a severe
slowdown in economic activity by constricting the supply of credit. In particular, the
usual mechanisms that allow nonfinancial firms to substitute away from bank loans and
other intermediated credit towards arms-length borrowing may become nonoperational in
times of a widespread and acute financial distress, especially given the crucial role that the
nonbank financial institutions play in originating, marketing, and guaranteeing debt issued
by the nonfinancial business sector.
4 Corporate Credit Spreads and Economic Activity
Credit spreads have long been used to gauge the degree of strains in the financial system.
Because asset prices are forward looking, movements in credit spreads have been shown
to be particularly useful for forecasting economic activity.2 Despite some success, results
from this strand of research are often sensitive to the choice of a credit spread index under
consideration. Moreover, credit spread indexes that contained useful information about
macroeconomic outcomes in the past often lose their predictive power for the subsequent
cyclical downturn. These mixed results are partly attributable to the rapid pace of financial
innovation that likely alters the forecasting power of financial asset prices over time or results
in one-off developments that may account for most of the forecasting power of a given credit
spread index.
To mitigate these problems, Gilchrist, Yankov, and Zakrajsek [2008] (GYZ hereafter)
rely on the prices of individual senior unsecured corporate debt issues traded in the sec-
ondary market to construct a broad array of corporate bond spread indexes that vary across
maturity and default risk. Compared with other corporate financial instruments, senior un-
secured bonds represent a class of securities with a long history containing a number of
2The predictive content of various corporate credit spreads for economic activity has been analyzed,among others, by Stock and Watson [1989]; Friedman and Kuttner [1998]; Gertler and Lown [1999]; Mueller[2007]; and King, Levin, and Perli [2007].
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Table 1: Predictive Content of Credit Spreads for Economic Activity
(Year-Ahead Forecast Horizon)
Real GDP Employment Ind. Production
Credit Spread Est. R2 Est. R
2 Est. R2
CP1m−Treas1m -0.119 0.048 -0.258 0.398 -0.189 0.132[0.98] - [2.40] - [1.41] -
Aaa−Treas10y -0.015 0.044 -0.132 0.351 -0.194 0.132[0.07] - [0.56] - [0.89] -
Baa−Treas10y -0.081 0.039 -0.243 0.370 -0.273 0.151[0.38] - [1.05] - [1.30] -
HY−Treas10y -0.435 0.147 -0.687 0.600 -0.581 0.298[1.49] - [3.56] - [2.28] -
EDF-Q1 -0.529 0.320 -0.602 0.702 -0.646 0.511[4.66] - [5.90] - [5.14] -
EDF-Q2 -0.588 0.374 -0.656 0.763 -0.723 0.583[4.80] - [7.66] - [5.65] -
EDF-Q3 -0.585 0.359 -0.653 0.752 -0.691 0.517[4.81] - [8.41] - [4.54] -
Note: For real GDP, sample period is 1990:Q2–2008:Q2 (T = 69). For private nonfarmemployment (EMP) and industrial production (IP), sample period is 1990:II–2008:VII(T = 210). Dependent variables are ∆4 ln(GDPt+4), ∆12 ln(EMPt+12), and ∆12 ln(IPt+12).Each regression specification includes a credit spread, a 4-quarter or a 12-month lag of therespective dependent variable, and a constant term (the latter two effects are not reported)and is estimated by OLS. Estimates of parameters corresponding to credit spreads arestandardized; absolute t-statistics reported in brackets are based on a heteroscedasticity-and autocorrelation-consistent asymptotic covariance matrix computed according to Neweyand West [1987].
business cycles. In addition, the rapid pace of financial innovation has done little to alter
the basic structure of these securities. Thus, the information content of spreads constructed
from yields on senior unsecured corporate bonds is likely to provide more consistent signals
regarding economic outcomes relative to spreads based on securities with a shorter history
or securities whose structure or the relevant market has underwent a significant structural
change. In addition, GYZ rely on the firm-specific expected default frequencies (EDFs)
provided by the Moody’s/KMV corporation to construct their credit spread indexes. Be-
cause they are based primarily on observable information in equity markets, EDFs provide
a more objective and more timely assessment of firm-specific credit risk compared with the
issuer’s senior unsecured credit rating.
The results in Table 1 examine the predictive content of various corporate credit spread
12
indexes for the following three measures of economic activity: real GDP, private nonfarm
payroll employment, and industrial production. Specifically, we estimate a simple fore-
casting equation in which the year-ahead growth in an indicator of economic activity is
regressed on its own value lagged one year and the current value of a credit spread. The
entries in the table correspond to the standardized coefficient estimates (with t-statistics
in brackets) on the effect of the credit spread on each measure of economic activity, along
with the explanatory power of the regression as measured by the adjusted R2.
The first four regressions employ standard credit spread indexes emphasized in
this literature. These include the one month commercial paper Treasury bill spread
(CP1mo−Treas1mo); the Aaa corporate bond spread (Aaa−Treas10y), the Baa corporate
bond spread (Baa−Treas10y); and the high-yield corporate bond spread (HY−Treas10y).3
According to the entries in the table, the standard credit spread indexes—with the excep-
tion of the high-yield bond spread, which contains substantial explanatory power for the
year-ahead growth in employment—contain very little information regarding the future di-
rection in economic activity. The next set of forecasting regressions relies on a subset of
the corporate bond spread indexes constructed by GYZ. Specifically, we focus on the credit
spread indexes for which GYZ document the highest predictive content, both in and out
of sample—that is, credit spreads constructed from very long-maturity bonds (remaining
term-to-maturity greater than 15 years) issued by firms in the low- and intermediate-risk
categories as defined by the lowest three quintiles of the EDF distribution (EDF-Q1, EDF-
Q2, and EDF-Q3). Compared with the standard default-risk indicators, the EDF-based
credit spread indexes contain significant predictive power for all three measures of eco-
nomic activity. The coefficients on the EDF-based credit spreads are always statistically
and economically significant, and the EDF-based credit spreads generate an in-sample fit
that is substantially above that obtained from regressions that rely on the standard credit
spread indexes.
Table 2 focuses on the predictive content of these credit spread indexes for total business
fixed investment spending and its major components—namely, equipment and software
(E&S, excluding high tech); high-tech equipment; and nonresidential structures. Again,
the paper-bill spread and the credit spread indexes based on Aaa- and Baa-rated long-term
corporate bonds have very little explanatory power for total investment spending or its
major components. The high-yield bond spread does forecast total investment spending,
though not nearly as well as the EDF-based credit spreads. The high-yield spread appears
3Commercial paper rates are taken from the “Commercial Paper Rates and Outstanding” Federal Reservestatistical release. The source of Treasury yields and yields on Aaa- and Baa-rated corporate bonds is“Selected Interest Rates” (H.15) Federal Reserve statistical release. To construct the high-yield spread,we use the High-Yield Master II index, a commonly used benchmark index for long-term speculative-gradecorporate bonds administered by Merrill Lynch.
13
Table 2: Predictive Content of Credit Spreads for Business Fixed Investment
(Year-Ahead Forecast Horizon)
INV-TOT INV-ES INV-HT INV-NRS
Credit Spread Est. R2 Est. R
2 Est. R2 Est. R
2
CP1m−Treas1m -0.277 0.343 -0.291 0.273 -0.049 0.337 -0.253 0.160[2.32] - [2.14] - [0.30] - [1.66] -
Aaa−Treas10y -0.286 0.343 -0.016 0.186 -0.393 0.467 -0.385 0.231[0.98] - [0.08] - [1.59] - [1.31] -
Baa−Treas10y -0.334 0.337 -0.032 0.187 -0.409 0.434 -0.455 0.243[1.00] - [0.18] - [1.33] - [1.62] -
HY−Treas10y -0.812 0.559 -0.357 0.254 -0.289 0.387 -0.870 0.598[2.94] - [1.30] - [0.94] - [6.15] -
EDF-Q1 -0.610 0.653 -0.740 0.695 -0.506 0.598 -0.288 0.182[4.85] - [6.49] - [3.01] - [1.52] -
EDF-Q2 -0.656 0.692 -0.791 0.721 -0.532 0.607 -0.351 0.226[5.08] - [8.87] - [2.95] - [1.83] -
EDF-Q3 -0.662 0.677 -0.827 0.732 -0.522 0.581 -0.347 0.224[4.71] - [11.4] - [2.55] - [1.69] -
Note: Sample period: quarterly data from 1990:Q2 to 2008:Q2 (T = 69). Dependent variables are∆4 ln(INVt+4), where INV denotes real investment spending in the following categories: INV-TOT =total business fixed investment; INV-ES = equipment and software (excluding high tech); INV-HT =high-tech; and INV-NRS = nonresidential structures. Each regression specification includes a creditspread, a 4-quarter lag of the respective dependent variable, and a constant term (the latter two ef-fects are not reported) and is estimated by OLS. Estimates of parameters corresponding to creditspreads are standardized; absolute t-statistics reported in brackets are based on a heteroscedasticity-and autocorrelation-consistent asymptotic covariance matrix computed according to Newey and West[1987].
to contain substantial predictive power for investment in nonresidential structures, whereas
the EDF-based credit spreads forecast E&S and high-tech investment, but they have limited
information content for future expenditures on nonresidential structures.
Although not reported, our results indicate that corporate credit spreads have essentially
no information content for future consumption spending on both durable and nondurable
goods as well as for residential investment. This lack of predictive power holds true for both
the standard credit spread indexes and the EDF-based default risk indicators considered by
GYZ. Thus, corporate credit spreads do well at predicting business spending but contain
little information for household spending.
According to Gertler and Lown [1999], the predictive content of credit spreads for eco-
nomic activity may be due to the presence of an operative financial accelerator mechanism
14
linking balance sheet conditions to the real economy through movements in the external
finance premium. As emphasized by Philippon [2008], however, the forecasting ability of
credit spreads may also reflect the fact that asset prices contain information about future
economic fundamentals in a world without financial market imperfections. In particular, as
economic fundamentals deteriorate, default risk will rise, even if defaults impose no addi-
tional dead-weight loss on the economy. According to this view, an increase in credit spreads
may reflect a reduction in expected future profits during an impending cyclical downturn
and does not necessarily provide a causal link by which movements in the external finance
premium either amplify economic disturbances originating in the real economy or exert
an independent effect on economic activity through financial disruptions that reduce the
supply of credit.
As argued by Gilchrist, Ortiz, and Zakrajsek [2008], one potential way to parse move-
ments in credit spreads between fluctuations owing to financial market imperfections and
swings reflecting solely the changes in expected default risk absent financial frictions is to
use a pricing model for corporate debt to measure deviations between the current level of
spreads and the level of spreads that should prevail assuming the usual pricing of default
risk. For example, if default becomes more costly during an economic downturn, one would
expect credit spreads to rise relative to an increase in expected default risk. Thus, devia-
tions of credit spreads from those predicted by a standard pricing model may provide direct
information about movements in the external finance premium over the business cycle.
Table 3 reports the results of such an exercise. In particular, using the monthly panel
data set of individual senior unsecured bond issues described in GYZ, we regress the cor-
porate bond spreads in month t on the expected default risk, as measured by the issuer’s
year-ahead EDF at the end of month t − 1; the regression specification also includes the
bond’s duration and par size to control for term and liquidity premiums. As shown in
column 1 of the table, the coefficient on the EDF is economically large and highly statis-
tically significant. Moreover, this reduced-form pricing model explains about 46 percent of
the variation in corporate bond spreads. Columns 2 and 3 report regression results that
allow for a nonlinear relationship between credit spreads and expected default risk, whereas
column 4—in addition to equity-based indicators of default-risk—also includes a full set
of credit rating dummies. As evidenced by the entries in the table, allowing for nonlinear
effects of expected default risk on credit spreads provides an incremental improvement in
the explanatory power of the regression. In contrast, the inclusion credit ratings fixed ef-
fects leads to a substantial increase in the goodness of fit. The specification that allows for
both nonlinearities in the relationship between credit spreads and expected default risk and
credit ratings fixed effects explains almost 62 percent of the variation in corporate bond
spreads.
15
Table 3: Corporate Bond Spreads and Expected Default Risk
Regression Specification
Explanatory Variable (1) (2) (3) (4)
ln(PARVALUE) 0.088 0.080 0.077 0.032(0.016) (0.016) (0.016) (0.014)
ln(DURATION) -0.002 0.004 0.005 0.050(0.104) (0.101) (0.010) (0.008)
ln(EDF−1) 0.362 0.428 0.487 0.293(0.010) (0.016) (0.015) (0.012)
ln(EDF−1)2 - 0.035 0.012 0.004
(0.005) (0.006) (0.004)ln(EDF−1)
3 - - -0.012 -0.008(0.002) (0.001)
Adj. R2 0.459 0.472 0.477 0.618
Industry Effectsa yes yes yes yes(0.000) (0.000) (0.000) (0.000)
Ratings Effectsb no no no yes(0.000)
Note: Sample period: monthly bond-level data from February 1990 toJuly 2008 (Obs. = 281,179). Dependent variable is the log of the credit spreadin month t. All specifications are estimated by OLS. Asymptotic robust standarderrors are clustered at the firm level and are reported in parentheses.
ap-values for the test of the null hypothesis of the absence of fixed industryeffects are reported in parentheses.
bp-values for the test of the null hypothesis of the absence of fixed credit ratingeffects are reported in parentheses.
We use the reduced-form pricing model in column 4 to construct the residual or pric-
ing error for each bond/month observations in the GYZ data set. We then calculate the
average of these pricing errors for each period to obtain an index that essentially removes
the average effect of movements in expected default risk on corporate bond spreads. As in
Gilchrist, Ortiz, and Zakrajsek [2008], we label this index the external finance premium.
Figure 4 shows the estimated external finance premium along with, for comparison pur-
poses, the credit spread for very long maturity bonds in the second quintile of the EDF
distribution constructed by GYZ (EDF-Q2), and the standard high-yield credit spread in-
dex (HY−Treas10y).4 All three series show substantial variation and comovement over the
business cycle, although the high-yield spread is considerably more volatile than the other
4In order to cast the external finance premium index in units that are easily interpretable, we scaled theindex such that it is in the same units as the average corporate spread in the GYZ data set.
16
Figure 4: Corporate Bond Spreads and the External Finance Premium
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
200
400
600
800
1000
1200Basis points
High YieldEDF-Q2External Finance Premium
Monthly
Note: The black depicts the high-yield corporate bond spread (HY−Treas10y); the red line depictsthe credit spread associated with very long maturity corporate bonds in the second quintile of the EDFdistribution (EDF-Q2); and the red line depicts the estimated external finance premium (see text fordetails). The shaded vertical bars denote NBER-dated recessions.
two credit spread indexes. Focusing on the current period of financial turmoil, the esti-
mated external finance premium shot up in the middle of 2007 and—at just under 400 basis
points—is currently substantially above the peak reached at the end of 2000. Thus, although
measured default risk may have been significantly higher during the 2001–02 period, the
external finance premium is in fact higher in the current period than at any time in the
past 19 years.
We now examine the extent to which movements in the external finance premium help
predict the year-ahead growth in real GDP, private payroll employment, and industrial
production. Table 4 contains the results of this exercise; the regression specifications in
Table 4 are identical to those reported in Table 1, except that we now use the estimated
external finance premium in place of the credit spread indexes. As evidenced by the entries
in the table, the forecasting power of the external finance premium is nearly as good as that
of the EDF-based credit spread indexes. The coefficient on the external finance premium is
highly statistically and economically significant for all three measures of economic activity,
and the in-sample fit of all three specifications is almost as good as the highest adjusted R2
reported in Table 4. Overall these results suggest that a significant portion of the predictive
power of corporate bond spreads for economic activity likely reflects the information content
17
Table 4: Predictive Content of External Finance Premium for Economic Activity
(Year-Ahead Forecast Horizon)
Real GDP Employment Ind. Production
Explanatory Variable Est. R2 Est. R
2 Est. R2
External Finance Premium -0.560 0.294 -0.673 0.731 -0.733 0.534[2.79] - [5.64] - [3.88] -
Note: For real GDP, sample period is 1990:Q2–2008:Q2 (T = 69). For private nonfarm employ-ment (EMP) and industrial production (IP), sample period is 1990:II–2008:VII (T = 210). Dependentvariables are ∆4 ln(GDPt+4), ∆12 ln(EMPt+12), and ∆12 ln(IPt+12). Each regression specification in-cludes the estimated external finance premium (see text for details), a 4-quarter or a 12-month lag ofthe respective dependent variable, and a constant term (the latter two effects are not reported) and isestimated by OLS. Estimates of parameters corresponding to credit spreads are standardized; abso-lute t-statistics reported in brackets are based on a heteroscedasticity- and autocorrelation-consistentasymptotic covariance matrix computed according to Newey and West [1987].
of credit spreads for disruptions in financial markets or variation in the cost of default, two
factors that would cause credit spreads to widen relative to expected default risk prior to
an economic downturn.
5 DSGE Models with Financial-Real Linkages
The ability of credit spreads to predict economic activity suggests important linkages be-
tween financial conditions and macroeconomic outcomes. Quantifying these linkages, how-
ever, requires structural models of the macroeconomy that can distinguish between move-
ments in credit supply and demand and that can account for general equilibrium feedback
effects between developments in the financial and real sectors of the economy. Recent work
by Christiano, Motto, and Rostagno [2007], Queijo von Heideken [2008], Graeve [2008],
and Christensen and Dib [2008] seeks to quantify these mechanisms by estimating dynamic
stochastic equilibrium models that incorporate credit market imperfections through the
financial accelerator mechanism described in Carlstrom and Fuerst [1997] and Bernanke,
Gertler, and Gilchrist [1999] (BGG hereafter).5
Although details differ in terms of model estimation and shock specification, all of
these papers document an important role for financial factors in business cycle fluctua-
tions. Queijo von Heideken [2008], for example, shows that the ability of a model with a
rich array of real and nominal rigidities to fit both the U.S. and the Euro-area data im-
5In an alternative approach, Levin, Natalucci, and Zakrajsek [2006] employ firm-level data on creditspreads, EDFs, and leverage to estimate directly the structural parameters of the debt-contracting problemunderlying the financial accelerator model of BGG.
18
proves significantly if one allows for the presence of a financial accelerator mechanism; and
Christiano, Motto, and Rostagno [2007] demonstrate that shocks to the financial sector
have played an important role in economic fluctuations over the past two decades, both in
the United States and in Europe. Queijo von Heideken [2008], however, estimates a struc-
tural model that is identified without reliance on financial data and that does not allow
for shocks to the financial sector, whereas Christiano, Motto, and Rostagno [2007], though
allowing for a wide variety of shocks to the financial sector, do not estimate the parameters
governing the strength of the financial accelerator mechanism. To date, we are aware of no
empirical work that seeks to estimate simultaneously the key parameters of the financial
accelerator mechanism along with the shocks to the financial sector.
In this section, we briefly summarize the ongoing work by Gilchrist, Ortiz, and Zakrajsek
[2008] (GOZ hereafter) that attempts to fill this gap. In particular, GOZ use Bayesian max-
imum likelihood methods to estimates a dynamic New Keynesian model that incorporates
the financial accelerator discussed in BGG. The main innovation of their approach is that
they incorporate explicitly estimates of the external finance premium constructed from the
reduced-form pricing models of corporate debt. These proxies for the unobservable external
finance premium are used to identify the strength of the financial accelerator mechanism
and to measure the extent to which disruptions in financial markets have contributed to
fluctuations in the real economy during the last two decades.
For tractability, the model is kept purposefully simple. As in BGG, it allows for a house-
hold sector that consumes, saves, and makes labor-supply decisions; an investment goods
sector that transforms current output into capital via an adjustment cost mechanism; and
a retail sector that faces Calvo-style price rigidities that result in a standard New Keyne-
sian Phillips curve. The model also allows for both habit formation in consumption and
for higher-order adjustment costs in investment. These adjustment costs imply that asset
prices—the value of capital in place—increase during economic expansions. Monetary pol-
icy is conducted by a modified Taylor rule that assumes that the monetary authority, given
interest-rate smoothing, adjusts nominal short-term interest rates in response to changes in
current inflation and output growth.
As in BGG, the model also allows for an entrepreneurial sector that faces significant
credit market frictions in the process of owning and operating the existing capital stock.
These frictions give rise to an external finance premium that creates a wedge between the
required return on capital—the rate at which entrepreneurs can borrow to finance capital
accumulation—and the risk-free rate of return received by the household sector for its
savings. In this environment, an expansion in output causes an increase in the value of
assets in place and a rise in the entrepreneurial net worth. As entrepreneurs’ net worth
expands relative to their borrowing, the external finance premium falls, causing a further
19
increase in both asset values and investment demand. These general equilibrium feedback
effects, in turn, further amplify the financial accelerator mechanism.
The model is estimated using Bayesian maximum likelihood techniques over the period
1985:Q1–2008:Q2, using data on real GDP, business fixed investment, CPI inflation, the
nominal federal funds rate, and a risk spread derived from the large panel of issuer-level
credit spreads discussed above.6
The estimated model parameters include the degree of habit formation in consumption;
adjustment costs to investment; the response of inflation to the output gap in the Phillips
curve; and the coefficients that determine the monetary policy rule. GOZ also estimate
the elasticity of the external finance premium to changes in net worth, the key parameter
governing the strength of the financial accelerator. In addition to the standard set of
shocks to household preferences, technology, and monetary policy, the model allows for an
exogenous serially-correlated shock to the external finance premium. GOZ interpret this
shock as a disturbance to the financial sector that boosts the external finance premium
beyond the level warranted by the current economic conditions and the current stance of
monetary policy. Consistent with the recent work this area, the estimates of the model
parameters indicate an important macroeconomic role for financial market frictions, which
act as an amplification mechanism for real and nominal disturbances in the economy. The
results also suggests that disturbances that originate in the financial sector have significant
real consequences.
The left column of Figure 5 depicts the model dynamics in response to a one standard
deviation (negative) shock to monetary policy rule, whereas the right column shows the
impulse responses to a one standard deviation (negative) shock to the external finance
premium. Unanticipated expansionary monetary policy causes an increase in output and
investment and a rise in inflation (not shown). It also causes a reduction in the external
finance premium, which serves to strengthen the monetary transmission channel through
the amplification mechanism described above. Similarly, a reduction in the external finance
premium also causes an expansion in investment and output and a fall in inflation (not
shown) through the supply-side benefits of increased capital accumulation. The real effect
of this mechanism is quantitatively large—a 20 basis point decline in the external finance
premium causes an 80 basis point increase in output.
To understand the implications of the model for the conduct of monetary policy and to
6The estimation period uses data prior to 1990, the first year for which monthly estimates of expecteddefault frequencies from Moody’s/KMV are available. Consequently, we are unable to use our proxy for theexternal finance premium derived from the reduced-form pricing model discussed above. We are currently inthe process of estimating the Merton [1974] distance-to-default bond pricing model for the entire nonfinancialcorporate sector going back to 1980, which will allows us to construct a similar estimate of the external financepremium over a longer period. For the purposes of this exercise, we constructed the risk spread by extractingthe first principal component from credit spreads in a large number of credit-risk portfolios.
20
Figure 5: Model Responses to Selected Shocks
Monetary Policy Shock Financial Shock
0 4 8 12 16 20 24 28 32 36 40
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1Percentage points
Quarters after shock
Federal funds rate
0 4 8 12 16 20 24 28 32 36 40
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1Percentage points
Quarters after shock
0 4 8 12 16 20 24 28 32 36 40
−0.3
−0.2
−0.1
0.0
0.1
0.2Percentage points
Quarters after shock
Risk spread
0 4 8 12 16 20 24 28 32 36 40
−0.3
−0.2
−0.1
0.0
0.1
0.2Percentage points
Quarters after shock
0 4 8 12 16 20 24 28 32 36 40
−0.1
0.0
0.1
0.2
0.3
0.4
0.5Percentage points
Quarters after shock
Output growth
0 4 8 12 16 20 24 28 32 36 40
−0.1
0.0
0.1
0.2
0.3
0.4
0.5Percentage points
Quarters after shock
0 4 8 12 16 20 24 28 32 36 40
−0.2
0.0
0.2
0.4
0.6
0.8
1.0Percentage points
Quarters after shock
Output growth
0 4 8 12 16 20 24 28 32 36 40
−0.2
0.0
0.2
0.4
0.6
0.8
1.0Percentage points
Quarters after shock
0 4 8 12 16 20 24 28 32 36 40
−0.5
0.0
0.5
1.0
1.5
2.0Percentage points
Quarters after shock
Investment growth
0 4 8 12 16 20 24 28 32 36 40
−0.5
0.0
0.5
1.0
1.5
2.0Percentage points
Quarters after shock
0 4 8 12 16 20 24 28 32 36 40
−1
0
1
2
3
4Percentage points
Quarters after shock
Investment growth
0 4 8 12 16 20 24 28 32 36 40
−1
0
1
2
3
4Percentage points
Quarters after shock
Note: The red lines in each panel depicts the estimated impulse responses of selected variablesto monetary policy shock (the left column) and to the financial shock (the right column). The shadedbands denote the 80 percent confidence intervals.
evaluate the importance of financial market frictions in determining business cycle outcomes,
we calculate the portion of the movements in the actual growth of output and investment,
nominal federal funds rate, and the risk spread that can be accounted for by monetary
policy innovations and shocks to the supply of credit. Figures 6–7 contain the result of
this exercise. Each panel of these two figures, shows the actual series—in percentage point
deviations from steady state—along with the estimated contribution of the two shocks.
Figure 6 summarizes the effects of a monetary policy shock, whereas Figure 7 focuses on
21
Figure 6: Historical Decomposition of Monetary Policy Shocks
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−2
−1
0
1
2
3Percentage points
ActualContribution of monetary policy shock
Output growth
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−5
0
5
10
15Percentage points
Investment growth
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−3
−2
−1
0
1
2
3
4Percentage points
Federal funds rate
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007−1.0
−0.5
0.0
0.5
1.0Percentage points
Risk spread
Note: The solid black lines in each panel depicts the behavior of actual variables expressed inpercentage point deviations from the steady state. The dotted red line in each panel depict the estimatedeffect of monetary policy shocks (see text for details). The shaded vertical bars denote NBER-datedrecessions.
22
Figure 7: Historical Decomposition of Financial Shocks
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−2
−1
0
1
2
3
4Percentage points
ActualContribution of financial shock
Output growth
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−10
−5
0
5
10
15Percentage points
Investment growth
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−3
−2
−1
0
1
2
3
4Percentage points
Federal funds rate
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0Percentage points
Risk spread
Note: The solid black lines in each panel depicts the behavior of actual variables expressed inpercentage point deviations from the steady state. The dotted red line in each panel depict the estimatedeffect of financial shocks (see text for details). The shaded vertical bars denote NBER-dated recessions.
23
the financial shock.
As shown in the four panels of Figure 6, the effect of monetary policy shocks on the
economy accord well with the historical record regarding the conduct of monetary policy
since the mid-1980s. Monetary policy was tight in the late 1980s prior to the onset of 1990–
91 recession but was eased substantially during the economic downturn of the early 1990s.
According to our estimates, tight monetary policy also contributed to the slowdown in
business investment and output during the 1994–95 period. The stance of monetary policy
was roughly neutral up through the collapse of the stock market in early 2000, and according
to our estimates, policy was eased significantly during the 2001 recession. Monetary policy
was again relatively tight during the housing boom of the 2005–07. The rapid sequence of
cuts in the federal funds rate during 2007 also appears as a significant easing of monetary
conditions that has supported expansion in investment and output during that period. An
appealing feature of this model is that the monetary transmission mechanism works in part
through its impact on balance sheet conditions—that is, the external finance premium is
strongly countercyclical in response to monetary policy shocks.
The estimated effects of financial disturbances and their impact on the real economy
also accord well with historical perceptions of the likely effects of tight credit conditions on
economic activity. According to our estimates, the economy showed signs of financial distress
at the onset of the 1990–91 recession, and adverse financial conditions remained a drag on
the real economy throughout the “jobless” recovery of the early 1990s. Indeed, between
1989 and 1993, shocks to the financial sector caused the external finance premium to rise by
150 basis points, an increase that led to an extended period of subpar economic performance.
Credit conditions improved markedly during the second half of the 1990s, a period during
which the external finance premium fell about 250 basis points. The premium moved higher
after the bursting of the “dot-com” bubble, and financial conditions deteriorated further at
the onset of the collapse in the housing sector in 2005. The model also captures the current
financial crisis as a shock to the financial sector, manifested as a 75 basis point jump in
the external finance premium that has led to a sharp slowdown in the growth of investment
and output during the last four quarters.
In summary, this relatively simple model of the financial accelerator—when estimated
using both real and financial market data—does remarkably well at capturing much of the
historical narrative regarding the conduct of monetary policy and developments in financial
markets that led to episodes of financial excess and distress over the last two decades. De-
spite this apparent success, it would be undoubtedly useful to expand the current model to
incorporate additional real-side and nominal frictions such as variable capacity utilization
and sticky nominal wages, combined with wage and price indexation. Another useful direc-
tion of this line of research would be to enrich the financial sector by introducing frictions
24
in the intermediation process that links household borrowing to house prices as in Aoki,
Proudman, and Vlieghe [2004] and Iacoviello [2005] and by explicitly modeling the financial
sector and the role that financial capital may have on the real economy. These extensions
of the basic framework are necessary both for empirical realism and to capture what are
undoubtedly the main concerns of policymakers today, namely the ongoing collapse of house
prices and its impact on the real economy through its effect on the value of assets held by
financial institutions.
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