Credit Risk and the Macroeconomy:
Evidence from an Estimated DSGE Model
Simon Gilchrist∗

Alberto Ortiz†

Egon Zakrajšek‡

May 25, 2009

Abstract
Canonical macroeconomic models have a difficult time accounting for the severity of
the feedback effects between financial conditions and the real economy during financial
crisis. Embedded in these models is the assumption of frictionless financial markets, implying that the composition of borrowers’ balance sheets has no effect on their spending
decision. Financial frictions—reflecting agency problems in credit markets—provide atheoretical link between the agents’ financial health and the amount of borrowing andhence economic activity in which they are able to engage. This paper attempts toquantify the role of such frictions in U.S. business cycle fluctuations during the 1973–
2008 period by estimating a DSGE model with the financial accelerator mechanism.
The main innovation of our approach is that we incorporate a high information-content
credit spread, constructed directly from the secondary-market prices of outstanding corporate bonds, into the Bayesian ML estimation. This high information-content credit
serves as a proxy for the fluctuations in the unobservable external finance premium, andits movements are used to identify the strength of the financial accelerator mechanism
and to quantify the magnitude of financial sector shocks. Our results indicate that
financial frictions are an economically important part of the propagation mechanism ofbusiness cycle shocks. An increase in the external finance premium causes a significant
drop in both investment and output. The estimated effects of financial disturbances
and their impact on the real economy also accord well with historical perceptions of thelikely effects of financial conditions on economic activity during the 1973–2008 period.
JEL Classification: E32, E44
Keywords: Credit spreads, financial accelerator, DSGE models

Robert Kurtzman and Oren Ziv provided outstanding research assistance. The views expressed in thispaper are solely the responsibility of the authors and should not be interpreted as reflecting the views of
the Board of Governors of the Federal Reserve System or of anyone else associated with the Federal Reserve
System.
∗Department of Economics Boston University and NBER. E-mail: sgilchri@bu.edu†
Department of Economics Oberlin College. E-mail: alberto.ortiz@oberlin.edu‡
Division of Monetary Affairs, Federal Reserve Board. E-mail: egon.zakrajsek@frb.gov

1

Introduction

The United States remains mired in the throes of an acute liquidity and credit crunch, by
all accounts, the severest financial crisis since the Great Depression.1 The roots of this crisis
lie in the bursting of the housing bubble, sparked by an unprecedented and unexpected fallin house prices. The resulting financial turmoil in mortgage markets subsequently spread toa variety of other asset markets, causing enormous liquidity problems in interbank funding
markets, a massive widening of yield-spreads on private debt instruments, a plunge in equity
prices, and a severe tightening of credit conditions for both businesses and households, aconfluence of factors that culminated in a sharp drop in economic activity. Indeed, by
the summer of 2008, a combination of a rapidly weakening U.S. economy and continued
turmoil in global credit markets led to a widespread loss of confidence in the financial
sector, and in the early autumn, the U.S. government intervened in the financial system atan unprecedented scale in order to prevent the incipient financial meltdown from engulfing
the real economy.2
The inability of canonical macroeconomic models to account for the severity of the feedback effects between financial conditions and the real economy during the current economic
downturn (as well as previous financial crisis) should come as no surprise. Predicated on
the Modigliani and Miller [1958] assumptions of frictionless financial markets, these models
imply that the composition of agents’ balance sheets has no effect on their optimal spending
decision: Households make consumption decisions based solely on permanent income—thesum of their financial wealth and the per-period income obtained from the present discounted value of future wages. And firms make investment decisions by comparing the
expected marginal profitability of new investment projects with the after-tax user-cost ofcapital, where the relevant interest rate reflects the maturity-adjusted risk-free rate of return appropriate to discount the future cash flows. Movements in financial asset prices thus
affect agents’ spending decisions insofar that they influence households’ financial wealth,and changes in interest rates affect spending decisions because they alter the present dis1

See Brunnermeier [2009] for an early account of the current financial crisis.
In September 2008, the government-sponsored enterprises Fannie Mae and Freddie Mac were placed
into conservatorship by their regulator; Lehman Brothers Holdings filed for bankruptcy; and the insurancecompany American International Group Inc. (AIG) came under severe pressure, necessitating the Federal
Reserve to provide substantial liquidity support to the company. At the same time, a number of otherfinancial institutions failed or were acquired by competitors. In response, U.S. government entities took
a number of measures to shore up financial markets, restore a degree of stability in the banking system,and support the flow of credit to businesses and households. In addition to large-scale capital injections,
expansions of deposit insurance, and guarantees of some forms of bank debt, these measures also included
the establishment of special lending programs to alleviate stresses in dollar funding markets, support thefunctioning of the commercial paper market, and restart certain securitization markets. For a history and afull description of these programs, see the Board of Governors’ website “Credit and Liquidity Programs andthe Balance Sheet,” available at http://www.federalreserve.gov/monetarypolicy/bst.htm.
2

1

counted values and hence appropriately calculated user-costs for financing real consumption
and investment expenditures.
Financial market imperfections—owing to asymmetric information or moral hazard on
the part of borrowers vis-à-vis lenders—provide a theoretical link between the agents’ financial health and the amount of borrowing and hence economic activity in which they are able
to engage. In general, contracts between borrowers and lenders require that borrowers post
collateral or maintain some stake in the project in order to mitigate the agency 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. Because this external financepremium is increasing in the amount borrowed relative to the borrower’s net worth andbecause 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 risein the premiums charged on the various forms of external finance. The increases in externalfinance premiums, in turn, lead to further cuts in spending and production. The resulting
slowdown in economic activity causes asset values to fall further and amplifies the economic
downturn—the so-called financial accelerator mechanism emphasized by Bernanke, Gertler,
and Gilchrist [1999] (BGG hereafter).3
In this paper, we attempt to quantify the role of the financial accelerator in U.S. business cycle fluctuations over the last three decades and a half. Our analysis consists of
two parts. First, we provide new empirical evidence on the relationship between corporate credit spreads—the difference in yields between various corporate debt instruments
3

Other 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]. In general, the various mechanisms linking balance sheet conditions of borrowers to real activity
fall under the guise of the “broad credit channel.” As underscored by the current financial crisis, financial
firms are also likely to suffer from asymmetric information and moral hazard problems when raising funds tofinance 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. As shown by Kashyap
and Stein [2000], this narrow credit channel appears to have important effects on the lending behavior ofsmaller banks. Such banks, however, account for only a small fraction of total bank lending in the UnitedStates, which suggests that the narrow credit channel may not be a quantitatively important transmission
mechanism of business cycle shocks. Reductions in bank capital during economic downturns can also reduce
lending activity. Banks seeking to shore up their capital or to meet regulatory capital requirements may
tighten their credit standards and cut back on lending, an inward shift in loan supply that curtails spendingof bank-dependent borrowers (see, for example, Van den Heuvel [2007].) The strength of this so-called“capital channel” depends on the overall health of the banking sector and on the extent to which firms andhouseholds are bank dependent. As evidenced by the sharp pullback in lending by large commercial banks
and nonbank financial institutions during the current financial crisis—owing to a lack of liquidity in theinterbank funding markets and the tightening of credit conditions as these institutions sought to replenish
depleted capital—the capital channel may have contributed importantly to the severity of the contractionin economic activity.

2and government securities of comparable maturity—and macroeconomic outcomes. In thecontext of the financial accelerator, an increase in default-risk indicators such as corporate
credit spreads curtails the ability of firms to obtain credit. The widening of credit spreads
could reflect disruptions in the supply of credit resulting from the worsening in the qualityof corporate balance sheets or the deterioration in the health of financial intermediaries
that supply credit. The resulting contraction in credit supply causes asset values to fall,incentives to default to increase, and yield spreads on private debt instruments to widen
further as lenders demand compensation for the expected increase in defaults.
Building on the recent work by Gilchrist, Yankov, and Zakrajšek [2009], we construct,using individual security-level data, a corporate credit spread index with a high information
content for future economic activity. Our forecasting results indicate that the predictive
content of this credit spread for various measures of economic activity significantly exceedsthat of widely-used financial indicators such as the standard Baa-Treasury corporate credit
spread and indicators of the stance of monetary policy such as the shape of the yield curve or
the real federal funds rate. However, as showed recently by Philippon [2009], the predictive
content of corporate bond spreads for economic activity could reflect—absent any financial
market imperfections—the ability of the bond market to signal more accurately than thestock market a general decline in economic fundamentals stemming from a reduction in
the expected present value of corporate cash flows prior to a cyclical downturn. This
result underscores the difficult identification issue that plagues empirical research aimed atquantifying the implications of credit supply shocks on the real economy: a fall in output
that follows a drop in lending associated with a major financial disruption reflects bothsupply and demand considerations.
In an attempt to disentangle movements in the supply and demand for credit, we impose
a structural framework on macroeconomic data by incorporating financial market frictions
into a dynamic stochastic general equilibrium (DSGE) model with a rich array of real andnominal rigidities. Specifically, we augment a dynamic New Keynesian model developed
by Smets and Wouters [2007] with the financial accelerator mechanism of BGG. We thenestimate the resulting model on U.S. quarterly data over the 1973–2008 period, an approachclosely related to the recent work of Christiano, Motto, and Rostagno [2008], De Graeve[2008], Christensen and Dib [2008], and Queijo von Heideken [2008], who showed that theability of DSGE models to fit macroeconomic data improves significantly if one allows forthe presence of a BGG-type financial accelerator mechanism.
The main innovation of our approach is that we incorporate our high information-content
credit spread directly in the Bayesian maximum likelihood (ML) estimation, where it serves
as a proxy for the fluctuations in the unobservable external finance premium.4 De Graeve
4

Whether observable credit spreads are a good proxy for the unobservable external finance premium

3[2008] and Queijo von Heideken [2008], in contrast, estimated a DSGE model with thefinancial accelerator that is identified without the reliance on financial data and that does
not allow for shocks to the financial sector, whereas Christiano, Motto, and Rostagno [2008],
though allowing for a wide variety of shocks to the financial sector, did not estimate theparameters governing the strength of the financial accelerator mechanism. In our estimation
approach, movements in the high information-content credit spread are used to identify thestrength of the financial accelerator mechanism in the DSGE framework and to measure
the extent to which disruptions in financial markets have contributed to fluctuations in thereal economy during the last three and a half decades.
Our estimates indicate that financial disturbances have played an economically significant role in U.S. business cycle fluctuations over the 1973–2008 period. In particular, themodel 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 portionof the observed slowdown in economic activity.
The remainder of the papers is organized as follows. Section 2 describes the constructionof our high information-content credit spread and compares its predictive power for economic
activity to that of some standard financial indicators. In Section 3, we augment the Smets
and Wouters [2007] model with the financial accelerator, discuss its estimation, and present
our main findings. Section 4 concludes.

2

Corporate Credit Spreads and Economic Activity

Corporate credit spreads have long been used to gauge the degree of strains in the financial system. Moreover, because financial asset prices are forward looking, movements incredit spreads have been shown to be particularly useful for forecasting economic activity.5
Despite considerable success, results from this strand of research are often sensitive to theis, of course, model dependent. Employing firm-level data on credit spreads, EDFs, and leverage Levin,
Natalucci, and Zakrajšek [2006] estimated directly the structural parameters of the debt-contracting problem
underlying the financial accelerator model of BGG. According to their results, movements in credit spreads
are highly correlated—both across firms and across time—with fluctuations in the model-implied externalfinance premium.
5
The forecasting power of various corporate credit spreads for economic activity has been analyzed, among
other, by Stock and Watson [1989]; Friedman and Kuttner [1998]; Duca [1999]; Emery [1999]; Gertler and
Lown [1999]; Ewing, Lynch, and Payne [2003]; Mody and Taylor [2004]; and Mueller [2007]. In addition,
Stock and Watson [2002b] have pointed out the ability of credit spreads to forecast economic growth using
dynamic factor analysis, and King, Levin, and Perli [2007] find that corporate bond spread indexes contain
important information about the near-term likelihood of a recession.

4

choice of a credit spread index under consideration, as credit spreads that contained useful
information about macroeconomic outcomes in the past often lose their predictive power forthe subsequent cyclical downturn.6 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.
In part to address these problems, Gilchrist, Yankov, and Zakrajšek [2009] (GYZ hereafter) rely on the prices of individual senior unsecured corporate debt issues traded in thesecondary market to construct a broad array of corporate bond spread indexes that varyacross maturity and default risk. Compared with other corporate financial instruments, senior unsecured bonds represent a class of securities with a long history containing a numberof business cycles, and the rapid pace of financial innovation over the past two decades hasdone 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 undergone
a significant structural change. In addition, GYZ rely on the firm-specific expected default
frequencies (EDFs) based on the option-theoretic framework of Merton [1974] provided by
the Moody’s/KMV corporation to construct their credit spread indexes. Because they arebased 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 seniorunsecured credit rating.
The results of GYZ indicate that at longer forecast horizons (i.e., one- to two-year
ahead), the predictive ability of their EDF-based portfolios of credit spreads significantly
exceeds—both in-sample and out-of-sample—that of the commonly-used default-risk indicators, such as the paper-bill spread and the Baa or the high-yield corporate credit spreadindexes. The predictive power of corporate bond spreads for economic activity comes fromthe upper-end and the middle of the credit quality spectrum and is concentrated in securities with a long remaining term-to-maturity. In this section, we construct such a highinformation-content default-risk indicator by constructing a credit spread of long-maturity
bonds issued by firms with low to medium probability of default. We compare its forecasting
6

For example, the spread of yields between nonfinancial commercial paper and comparable-maturity
Treasury bills—the so-called paper-bill spread—has lost much of its forecasting power since the early 1990s.
Indeed, according to Thoma and Gray [1998] and Emery [1999], the predictive content of the paper-billspread may have reflected a one-time event. Similarly, yield spreads based on indexes of high-yield corporate
bonds, which contain information from markets that were not in existence prior to the mid-1980s, have done
particularly well at forecasting output growth during the previous decade, according to Gertler and Lown
[1999] and Mody and Taylor [2004]. Stock and Watson [2003], however, find mixed evidence for the high-yieldspread as a leading indicator during this period, largely because it falsely predicted an economic downturnin the autumn of 1998.

5

ability over the 1973–2008 period to that of the standard Baa-Treasury credit spread andother financial indicators such as the slope of the yield curve and the federal funds rate. We
then use our high information-content credit spread as a proxy for the unobservable externalfinance premium in the Bayesian ML estimation of a DSGE model that incorporates thefinancial accelerator mechanism.

2.1

Corporate Bond Spreads

The key information for our analysis comes from a large sample of fixed income securities
issued by U.S. nonfinancial corporations. Specifically, for a sample of 926 publicly-tradedfirms covered by the Center for Research in Security Prices (CRSP) and S&P’s Compustat,month-end secondary market prices of their outstanding long-term corporate bonds weredrawn from the Lehman/Warga (LW) and Merrill Lynch (ML) databases. These two data
sources include secondary market prices for a significant fraction of dollar-denominatedbonds publicly issued in the U.S. corporate cash market. The ML database is a proprietarydata source of daily bond prices that starts in 1997. Focused on the most liquid securities inthe secondary market, bonds in the ML database must have a remaining term-to-maturityof at least two years, a fixed coupon schedule, and a minimum amount outstanding of$100 million for below investment-grade and $150 million for investment-grade issuers. By
contrast, the LW database of month-end bond prices has a somewhat broader coverage andis available from 1973 through mid-1998 (see Warga [1991] for details).
To ensure that we are measuring long-term financing costs of different firms at the same
point in their capital structure, we limited our sample to only senior unsecured issues.
For the securities carrying the senior unsecured rating and with market prices in both the
LW and LM databases, we spliced the option-adjusted effective yields at month-end—acomponent of the bond’s yield that is not attributable to embedded options—across thetwo data sources. To calculate the credit spreads at each point in time, we matched theyield on each individual security issued by the firm to the estimated yield on the Treasury
coupon security of the same maturity. The month-end Treasury yields were taken from thedaily estimates of the U.S. Treasury yield curve reported in Gürkaynak, Sack, and Wright[2007]. To mitigate the effect of outliers on our analysis, we eliminated all observations withcredit spreads below 10 basis points and with spreads greater than 5,000 basis points. This
selection criterion yielded a sample of 5,269 individual securities between January 1973 andDecember 2008.
Table 1 contains summary statistics for the key characteristics of bonds in our sample.
Note that a typical firm has only a few senior unsecured issues outstanding at any point
in time—the median firm, for example, has two such issues trading at any given month.
This distribution, however, exhibits a significant positive skew, as some firms can have6

as many as 75 different senior unsecured bond issues trading in the market at a point intime. The distribution of the real market values of these issues is similarly skewed, with therange running from $1.1 million to more than $6.6 billion. Not surprisingly, the maturityof these debt instruments is fairly long, with the average maturity at issue of 14 years.
Because corporate bonds typically generate significant cash flow in the form of regularcoupon payments, the effective duration is considerably shorter, with both the average andthe median duration of about 6 years. Although our sample spans the entire spectrumof credit quality—from “single D” to “triple A”—the median bond/month observation, at“A3,” is solidly in the investment-grade category.
Turning to returns, the (nominal) coupon rate on these bonds averaged 7.77 percent
during our sample period, while the average total nominal return, as measured by the nominal effective yield, was 8.26 percent per annum. Reflecting the wide range of credit quality,
the distribution of nominal yields is quite wide, with the minimum of about 1.2 percent
and the maximum of more than 57 percent. Relative to Treasuries, an average bond in
our sample generated a return of about 178 basis points above the comparable-maturity
risk-free rate, with the standard deviation of 275 basis points.

2.2

Credit Risk Indicators

As noted above, our aim is to construct a portfolio of long-maturity corporate bonds issued
by firms with a low to medium probability of default. To measure a firm’s probabilityof default at each point in time, we employ the “distance-to-default” (DD) framework
developed in the seminal work of Merton [1973, 1974]. The key insight of this approach isthat the equity of the firm can be viewed as a call option on the underlying value of thefirm with a strike price equal to the face value of the firm’s debt. Although neither theunderlying value of the firm nor its volatility can be directly observed, they can, under theassumptions of the model, be inferred from the value of the firm’s equity, the volatility of
its equity, and the firm’s observed capital structure.
The first critical assumption underlying the DD-framework is that the total value of thea firm—denoted by V —follows a geometric Brownian motion:dV = µV V dt + σV V dW,(1)

where µV is the expected continuously compounded return on V ; σV is the volatility offirm value; and dW is an increment of the standard Weiner process. The second criticalassumption pertains to the firm’s capital structure. In particular, it is assumed that thefirm has just issued a single discount bond in the amount D that will mature in T periods.
Together, these two assumption imply that the value of the firm’s equity E can be viewed as7

a call option on the underlying value of the firm V with a strike price equal to the face valueof the firm’s debt D and a time-to-maturity of T . According to the Black-Scholes-Merton
option-pricing framework, the value of the firm’s equity then satisfies:
E = V Φ(δ1 ) − e−rT DΦ(δ2 ),(2)

where r denotes the instantaneous risk-free interest rate, Φ(·) is the cumulative standard
normal distribution function, andδ1 =ln(V /D) + (r + 0.5σV2 )T√
σV2 Tand δ2 = δ1 − σV√T.

According to equation 2, the value of the firm’s equity depends on the total value of thefirm and time, a relationship that also underpins the link between volatility of the firm’svalue σV and the volatility of its equity σE . In particular, it follows from Ito’s lemma that

V ∂E
σV .
σE =
E ∂V


(3)

∂E
Because under the Black-Scholes-Merton option-pricing framework ∂V= Φ(δ1 ), the rela-tionship between the volatility of the firm’s value and the volatility of its equity is givenbyV
Φ(δ1 )σV .
σE =
E


(4)

From an operational standpoint, the most critical inputs to the Merton DD-model are
clearly the market value of the equity E, the face value of the debt D, and the volatility ofequity σE . Assuming a forecasting horizon of one year (T = 1), we implement the modelin two steps: First, we estimate σE from historical daily stock returns. Second, we assume
that the face value of the firm’s debt D is equal to the sum of the firm’s current liabilities
and one-half of its long-term liabilities.7 Using the observed values of E, D, σE , and r (i.e.,the one-year constant-maturity Treasury yield), equations 2 and 4 can be solved for V andσV using standard numerical techniques. However, as pointed out by Crosbie and Bohn
[2003] and Vassalou and Xing [2004], the excessive volatility of market leverage (V /E) inequation 4 causes large swings in the estimated volatility of the firm’s value σV , which are
difficult to reconcile with the observed frequency of defaults and movements in financial
asset prices.
7

This assumption for the “default point” is also used by Moody’s/KMV in the construction of their
Expected Default Frequencies (EDFs) based on the Merton DD-model, and it reflects the finding that mostdefaults occur when the market value of the firm’s assets drops below the sum of its current liabilities
and one-half of its long-term liabilities. Both current and long-term liabilities are taken from quarterly
Compustat files and interpolated to daily frequency using a step function.

8

To resolve this problem, we implement an iterative procedure recently proposed by
Bharath and Shumway [2008].8 The resulting solutions of the Merton DD-model can beused to calculate the firm-specific distance-to-default over the one-year horizon as
DD =ln(V /D) + (µV − 0.5σV2 )
,
σV

(5)

where µV is an estimate of the expected annual return on the firm’s assets. The corresponding implied probability of default—the so-called EDF— is given by ln(V /D) + (µV − 0.5σV2 )EDF = Φ −= Φ(−DD),σV(6)which, under the assumptions of the Merton model, should be a sufficient statistic forpredicting defaults.

2.3High Information-Content Corporate Credit Spread

We construct a proxy for the external finance premium—namely, a medium-risk, longmaturity corporate credit spread—by sorting our sample of credit spreads into a “mediumrisk” category based on the distribution of the estimated firm-specific DDs in month t − 1.

In particular, for each month t, we calculate the 20th and 60th percentiles of the crosssectional distribution of the distance-to-default using a sample of all publicly-traded firms
in the matched CRSP/Compustat sample (more than 2,700 firms in an average month).
The resulting time-varying thresholds define a “medium-risk” category for our sample of926 bond issuers. Within this credit risk category, we then select all bonds with the remaining term-to-maturity of more than 15 years and compute an arithmetic average of credit
spreads for each month t.
Figure 1 shows our medium-risk, long-maturity credit spread along with, for comparison
purposes, the average credit spread for our sample of bonds, and the widely-used Baa corporate bond spread, defined as the difference between the yield on an index of seasoned
long-term Baa-rated corporate bonds and the yield on the constant-maturity 10-year Treasury note.9 All three series show substantial variation and comovement over the businesscycle. Whereas the Baa spread is highly correlated with the average credit spread for our
8

Briefly, the procedure involves the following steps. Initialize the procedure by letting σV = σE [D/(E +D)]. Use this value of σV in equation 2 to infer the market value of the firm’s assets V for every day of theprevious year. Then calculate the implied daily log-return on assets (i.e., ∆ln V ) and use the resulting series
to generate new estimates of σV and µV . Iterate on σV until convergence; see Bharath and Shumway [2008]
for details.
9
The source for all Treasury yields and the yield on Baa-rated long-term corporate bonds is “SelectedInterest Rates” (H.15) Federal Reserve statistical release. Because the Baa yield is only available at quarterly
frequency prior to 1986:Q1, we converted our monthly credit spreads to quarterly averages.

9

sample of bonds (ρ = 0.79), its correlation with the medium-risk, long maturity credit
spread is much lower (ρ = 0.55). Both the average spread and its Baa counterpart appear
considerably more volatile than the medium-risk, long maturity spread, especially during
economic downturns. Focusing on the current period of financial turmoil, all three credit
spreads started to move up noticeably in the second half of 2007, and they reached theirrespective historical peaks in the fourth quarter of 2008.

2.4

Forecasting Power of Credit Spreads for Economic Activity

This section examines the predictive power of our medium-risk, long-maturity credit spreadfor various measures of economic activity and compares its forecasting performance withthat of several commonly used financial indicators. Letting Yt denote a measure of economic
activity in quarter t, we define
400ln∆ Yt+h ≡
h
h



Yt+h
Yt



,

where h denotes the forecast horizon. We estimate the following univariate forecasting
specification:∆h Yt+h = α +h−1Xβi ∆Yt−i + γ1 TSt3M −10Y + γ2 RFFt + γ3 CSt + ǫt+h ,

(7)

i=0

where TSt3M −10Y denotes the “term spread”—that is, the slope of the Treasury yield curve
defined as the difference between the three-month constant-maturity Treasury yield and the
10-year constant-maturity yield;10 RFFt denotes the real federal funds rate;11 CSt denotes acredit spread; and ǫt+h is the forecast error. The forecasting regression given by equation 7
is estimated using OLS, and the serial correlation induced by overlapping forecast errors istaken into account by computing the covariance matrix of regression coefficients according
to Newey and West [1987], with the “lag truncation” parameter equal to h + 1.
In addition to predicting output and business fixed investment, we also consider labormarket and production indicators, such as private nonfarm payroll employment, the unemployment rate, and the index of manufacturing industrial production. Focusing on the
year-ahead horizon (i.e., h = 4), our first set of forecasting results uses data over the en10
The role of the term spread in forecasting economic growth or for assessing the near-term risk of recession
has been analyzed by Dotsey [1998], Estrella and Hardouvelis [1991], Estrella and Mishkin [1998], andHamilton and Kim [2002]. More recent work on this topic includes Ang, Piazzesi, and Wei [2006] andWright [2006].
11
In calculating the real federal funds rate, we employ a simplifying assumption that the expected inflation
is equal to lagged core PCE inflation. Specifically, real funds rate in quarter t is defined as the average
effective federal funds rate during quarter t less realized inflation, where realized inflation is given by the
log-difference between the core PCE price index and its lagged value 4 quarters earlier.

10

tire sample period (1973–2008). In light of the well-documented decline in macroeconomic
volatility since the mid-1980s, we also examine the predictive power of these financial indicators for economic activity during the so-called “Great Moderation,” namely over the
1986–2008 period.
2.4.1

Results: Sample Period 1973–2008

The results in Table 2 examine the predictive power of various financial asset prices forlabor market and production indicators over the entire sample period. According to thebaseline specification—shown in column 1—the slope of the yield curve is statistically andeconomically significant predictor for all three measures of economic activity, with the inversion of the yield curve signalling a slowdown in future economic activity. For example,
a one-percentage-point decline in the term spread in quarter t reduces the year-ahead employment growth by almost one-half percentage points, pushes the unemployment rate up
by more than one-quarter percentage points, and depresses the growth in industrial output
by about 1.2 percentage points. Conditional on the shape of the yield curve, tight monetary policy, as measured by the real federal funds rate, is also predictive of a subsequent
slowdown in economic activity.12
Column 2 shows the results in which the baseline specification is augmented with thestandard Baa corporate credit spread index. As evidenced by the entries in the table,
the Baa spread has no marginal predictive power for any of the three measures of economic
activity. In contrast, the credit spread based on long-maturity senior unsecured bonds issued
by medium-risk firms—column 3—is a highly significant predictor for both labor market
indicators and the growth of industrial output: a one-percentage-point rise in the mediumrisk, long-maturity spread in quarter t lowers employment growth over the subsequent four
quarters by almost 1.25 percentage points, boosts the unemployment rate up by almost onehalf percentage points, and cuts almost 2.5 percentage points form the year-ahead growth
in manufacturing industrial production.
Table 3 examines the predictive contents of these financial indicators for the growth ofreal GDP and real business fixed investment. According to the results in column 1, theshape of the yield curve contains substantial predictive power for the year-ahead growth12
Under the expectations hypothesis and neglecting term premiums, the term spread is an indicator of
the stance of monetary policy—the higher the term spread, the more restrictive is the current stance ofmonetary policy and, hence, the more likely is economy to decelerate in subsequent quarters. In general,
however, the shape of the yield curve contains information about term premiums and the average of expected
future short-term interest rates over a relatively long horizon. As emphasized by Hamilton and Kim [2002]
and Ang, Piazzesi, and Wei [2006], the term premium and expectations hypothesis components of the term
spread have very different correlations with future economic growth. The federal funds rate, in contrast, isa measure of the stance of monetary policy that is relatively unadulterated by the effects of time-varying
term premiums.

11

in real output, whereas the current stance of monetary policy—as measured by the real
federal funds rate—is highly informative for the subsequent growth in capital expenditures.
As before, the standard Baa credit spread index—column 2—has no marginal predictive
power for either activity indicator, whereas our medium-risk, long-maturity credit spread—column 3—appears to be a particularly good predictor of the growth in business fixed
investment. For example, a one-percentage-point widening in our credit spread leads to a
six-percentage-point drop in the year-ahead growth in real business fixed investment. In
contrast, the coefficient on the medium-risk, long-maturity credit spread in the forecasting
regression for output growth, although economically significant, is estimated rather imprecisely.
2.4.2

Results: Sample Period 1986–2008

In this section, we repeat our forecasting exercises for the Great Moderation period, namelyfrom 1986:Q1 onward. Although no clear consensus has emerged regarding the dominantcause(s) of the striking decline in macroeconomic volatility since the mid-1980s, changes in
the conduct of monetary policy appear to be at least partly responsible for the significantly
diminished variability of both output and inflation over the past two decades; see, forexample, Clarida, Galı́, and Gertler [2000] and Stock and Watson [2002a]. Because monetary
policy affects the real economy by influencing financial asset prices, the change in themonetary policy regime may have also altered the predictive content of various financial
indicators for economic activity. Moreover, as emphasized by Dynan, Elmendorf, and Sichel[2006], the rapid pace of financial innovation since the mid-1980s—namely, the deepening
and emergence of lending practices and credit markets that have enhanced the ability ofhouseholds and firms to borrow and changes in government policy such as the demise ofRegulation Q—may have also changed the information content of financial asset prices for
macroeconomic outcomes.13
Tables 4 and 5 contain our forecasting results for the 1986–2008 subperiod. As evidenced
by the entries in column 2 of Table 4, the standard Baa credit spread remains an insignificant
predictor—both statistically and economically—of all three measures of economic activity,a result consistent with that reported in Table 2. In contrast, our medium-risk, long13
Although a full formal investigation of potential parameter instability is beyond the scope of this paper,
we tested for time variation in the coefficients associated with financial indicators in the forecasting equation 7
using the methodology proposed by Elliott and Müller [2006]. These tests do not reject—even at the
10 percent level—the null hypothesis of fixed regression coefficients on financial indicators for all specifications
reported in Table 2. However, we reject at the 5 percent (or higher) significance level the null hypothesis of
fixed coefficients on financial indicators in the forecasting regressions for output growth and very much failto reject the same null in the forecasting regressions for the growth in business fixed investment (Table 3).
Thus the evidence suggests a stable relationship between our set of financial indicators and changes in labormarket conditions as well as growth in industrial output and investment, but points to some structural
instability in the relationship between asset prices and the growth in real GDP.

12

maturity credit spread—column 3—continues to provide significant information for the
future changes in labor market conditions and growth in industrial output. Indeed, theestimated coefficients on the medium-risk, long-maturity credit spread are noticeably larger(in absolute value) than those reported in Table 2, and the associated confidence intervals
are appreciably narrower compared with those estimated over the full sample period.
A similar pattern emerges when forecasting the growth of real GDP and business fixed
investment. As shown in column 3 of Table 5, the inclusion of the medium-risk, longmaturity credit spread in the baseline specification for the year-ahead growth in both output and investment yields a substantial improvement in the adjusted R2 s, and the credit
spread enters both specifications with a coefficients that is economically and statisticallyhighly significant. Moreover, the presence of the medium-risk, long-maturity credit spread
eliminates any predictive content of the term spread and the real federal funds rate for bothmeasures of economic activity. The inclusion of the standard Baa credit spread index, in
contrast, yields no improvement in the in-sample fit. All told, these results are consistent
with those reported by GYZ for the 1990-2008 period: The information content of corporate
credit spreads for macroeconomic outcomes is embedded in long-maturity bonds issued byfirms in the middle and the upper-end of the credit quality spectrum. Such credit spreads
hold substantial information content for broad measures of economic activity and are especially good predictors of economic activity during the Great Moderation, a period in which
they significantly outperform the forecasting ability of other financial indicators such as theshape of the yield curve and the real federal funds rate.

3

An Estimated DSGE Model with Financial Frictions

In this section, we describe the log-linearized version of the Smets and Wouters [2007] (SWhereafter) DSGE model extended to include the financial accelerator outlined in BGG.
The SW-model is a variant of a New Keynesian model that incorporates a rich array of
nominal and real rigidities, such as habit formation on the part of households, higherorder adjustment costs to investment, variable utilization in production, and Calvo-stylenominal price and wage rigidities with partial indexation. Monetary policy in the model isconducted according to a Taylor-type rule for the nominal interest rate. We first outline thebasic model without financial frictions and then describe the extension of the model that
includes the financial accelerator mechanism. We then discusses the estimation strategy
and present our main results.

13

3.1

The SW-Model without the Financial Accelerator

The resource constraint stipulates that the aggregate output yt depends on consumption
ct , investment it , resources lost owing to variable capital utilization zt , and an exogenous
disturbance (i.e., government spending) to the resource constraint εgt :yt = cy ct + iy it + zy zt + εgt .

(8)

The Cobb-Douglas production function relates output to a weighted average of capital
services kts , labor inputs lt , and an exogenous level of disembodied technology εat :yt = φp [αkts + (1 − α)lt + εat ] ,(9)where α measures the share of capital in production and φp equals one plus the share of
fixed costs in production. Capital services depend on the existing stock of capital kt−1 andthe capital-utilization rate zt , according to
kts = kt−1 + zt .

(10)

Under cost minimization, the marginal product of capital depends on the capital-labor ratio
and the real wage:
mpkt = − (kts − lt ) + wt ,(11)

whereas optimal capital utilization determines the relationship between the utilization rate
and the marginal product of capital:1−ψ
mpkt ;
zt =
ψ


(12)

the parameter ψ in equation 12 determines the elasticity of utilization costs with respect tocapital inputs.
The demand-side of the model specifies the two intertemporal Euler equations that determine the optimal consumption and investment decisions. In particular, let σc denote theintertemporal elasticity of substitution; λ the degree of habit formation; β the household’s
discount factor; γ the trend growth rate of technology; ϕ the cost of adjusting the rate ofhinvestment; and WC∗ ∗L∗ the steady-state ratio of labor income to consumption. Then the loglinearized consumption Euler equation implies that a weighted average of current, past, andexpected future consumption and labor is a function of the real interest rate (rt − Et (πt+1 ))14and the intertemporal shock to preferences εbt :

ct −

"

λ
γ

1 + λγ#ct−1 −"

1
1 + λγ# h 
(σc − 1) WC∗ ∗L∗  (lt − Et lt+1 ) =Et ct+1 − σc 1 + λγ



(13)

1 − λγ  (rt − Et πt+1 ) + εbt .
−λσc 1 + γ

Similarly, the Euler equation specifying the optimal investment trajectory implies that aweighted average of past, current, and future investment depends on the value of installed
capital qt :
"
#



1
1
1
βγ (1−σc )
it −
qt .
it−1 −
Et it+1 =1 + βγ (1−σc )1 + βγ (1−σc )1 + βγ (1−σc ) (γ 2 ϕ)

(14)

The arbitrage condition for the value of installed capital states that the value of capital today depends positively on the expected future marginal product of capital and the
expected future value of capital and negatively on the rate of return required by thehouseholds—that is, the real interest rate relative to the intertemporal shock to preferences:



1−δ
R∗K
Et mpkt+1 +Et qt+1
qt =R∗K + (1 − δ)R∗K + (1 − δ)σc 1 + λγ εbt ,− (rt − Et πt+1 ) + 
λ
1− γ


(15)

where δ denotes the rate of capital depreciation. Lastly, the log-linearized equation for
capital accumulation can be expressed as





1−δ
1−δ
kt =
kt−1 + 1 −
it .
γ
γ

(16)

We now consider the setting of prices and wages under the assumption of Calvo-typeadjustment mechanisms with partial indexation. Let µpt and µw
t denote the price and wage
markups, respectively, both of which are determined under monopolistic competition.14
Then,µpt ≡ mplt − wt = α(kts − lt ) + εat − wt ;(17)

Note that µpt = −mct , where mct denotes real marginal costs—that is, the price mark-up is inversely
related to marginal costs.
14

15

and

"

1
µw
t ≡ wt − mrst = wt − σl lt +1 − λγ#
λ.
ct − ct−1γ(18)Note that the wage mark-up in equation 18 measures the gap between the real wage and thehouseholds’ marginal rate of substitution between consumption and leisure. Letting ιp andιw measure the degree of price and wage indexation, respectively, then the New KeynesianPhillips curve implies that a weighted-average of current, past, and expected future inflation
depends on the price markup and an exogenous cost-push shock to prices εpt , according to"#ιpβγ (1−σc )πt−1 +
Et πt+1πt =
1 + βγ (1−σc ) ιp
1 + βγ (1−σc ) ιp
"
 !#

(1 − ξp ) 1 − βγ (1−σc ) ξp1−
µpt + εpt .
ξp ((φp − 1)ǫp + 1)
1 + βγ (1−σc ) ιp


(19)

Similarly, Calvo-style wage setting implies that a weighted-average of current, past, andexpected future wages depends on the wage-markup, inflation, and a cost-push shock to
wages εwt :"#βγ (1−σc )1
wt−1 +
wt =(Et wt+1 + Et πt+1 )1 + βγ (1−σc )1 + βγ (1−σc )
"
#


ιw
1 + βγ (1−σc ) ιw
−
π
+
πt−1
t
1 + βγ (1−σc )1 + βγ (1−σc )
"
 !#

(1 − ξw ) 1 − βγ (1−σc ) ξw
1w−
µw
t + εt .
ξw ((φw − 1) ǫw + 1)
1 + βγ (1−σc )(20)Finally, the model stipulates that the monetary authority follows a rule in setting theshort-term nominal interest rate rt . Specifically, the monetary policy rule allows the current
interest rate to respond to lagged interest rates, current inflation, the current level andchange in the output gap, and an exogenous policy disturbance εrt :

h
h
i
 
i
f
rt = ρrt−1 + (1 − ρ) rπ πt + ry yt − ytf + r∆y yt − ytf − yt−1 − yt−1
+ εrt ,

(21)

where ytf denotes potential output, defined as the output that would be obtained underfully flexible wages and prices.

3.2

Augmenting the SW-Model with the Financial Accelerator

The financial accelerator model in BGG centers on the entrepreneurial sector that buys
capital at price qt in period t and uses that capital in production in period t + 1. At
16

t + 1, entrepreneurs receive the proceeds—the marginal product of capital mpkt+1 —fromoperating their capital, which is then resold at price qt+1 . Under these assumptions, thecapital-arbitrage equation implies that the expected rate of return on capital in the entrepreneurial sector is given byk
Et rt+1
=






1−δ
R∗K
Et qt+1 +Et mpkt+1 − qt ,R∗K + (1 − δ)R∗K + (1 − δ)

(22)

where R∗K denotes the steady-state value of the return on capital in the model with thefinancial accelerator.15
Entrepreneurs are assumed to be risk neutral and, owing to an exogenous survival rate,
discount the future more heavily than the households. Entrepreneurs have access to net
worth nt , which they may use to finance a portion of their capital expenditures (qt kt ).
Financial frictions—reflecting the costly-state verification problem between entrepreneurs
and risk-neutral financial intermediaries—imply that entrepreneurs face an external financepremium st that drives a wedge between the expected return on capital and the expected
return demanded by households:kst = Et rt+1
− (rt − Et πt+1 )(23)

The presence of financial frictions also implies that the size of the external financepremium is negatively related to the strength of entrepreneurs’ balance sheets:
st = −χ (nt − qt − kt ) + εfd
t ,

(24)

where the coefficient χ > 0 measures the elasticity of the premium with respect to leverage.
We assume that the external finance premium also depends on an exogenous financial
disturbance εfd
t , which may be thought of as a shock to the supply of credit that captureschanges in the efficiency of the financial intermediation process. Equation 24 also implies
that the entrepreneurial sector leverages up to the point where the expected return on
capital equals the cost of borrowing in the external credit market.
Because we assumed that entrepreneurs are long-lived but discount the future moreheavily than households, entrepreneurial net worth depends on past net worth and on the
return on capital relative to the expected return:K
nt = rtk −
N




K
− 1 (st−1 + rt−1 − πt ) + θnt−1 + εnw
t ,
N

15

(25)

As discussed in the appendix, in the casefinancial accelerator, the steady-state values are modified` of´χthe
1 σcbecause R∗K changes from β1 γ σc −(1 − δ) to K−(1 − δ). In the flexible economy without the financialγNβ
accelerator case χf = 0.

17where K/N denotes the steady-state ratio of capital expenditures to entrepreneurial net
worth and θ is the survival rate of entrepreneurs. The first term in equation 25 reflects theleveraged realized return on net worth, whereas the second term represents the required
payment to the holders of debt. Rearranging, one can see that
nt =


K k
rt − Et−1 rtk + Et−1 rtk + θnt−1 + εnwt .
N

In the BGG framework, the presence of leverage thus magnifies the effect of unexpectedchanges in the return on capital on entrepreneurs’ net worth.
Finally, entrepreneurs that do not survive a given period are assumed to consume their
net worth:cet = nt ,(26)

which implies the following modification of the constraint on aggregate resources:
yt = cy ct + cey cet + iy it + zy zt + εgt ,(27)

where cey denotes the consumption-output ratio of entrepreneurs. In practice, however,this consumption-output ratio is small, so that movements in net worth have a negligible
effect on the resource constraint.16 To summarize, augmenting the SW-model with thefinancial accelerator includes the addition of the equations that determine the externalfinance premium—that is, equations 23 and 24—along with equation 25 that specifies theevolution of net worth. In addition, the original resource constraint equation 8 is modified
according to equation 27.

3.3

Shocks, Data, and Estimation

As in Smets and Wouters [2007], the model is estimated using time-series data for thequarterly growth rate of real output, consumption, investment, and real wages, along with
inflation, the log of hours worked, and the effective nominal federal funds rate. To quantify
the strength of financial frictions and identify disturbances to the financial sector, ourestimation procedure also includes the medium-risk, long-maturity corporate bond spread
discussed above. This high information-content financial indicator is used as a proxy forthe unobservable external finance premium st . Because corporate credit spreads exhibit adiscernible upward trend over our sample period, we linearly detrended our proxy for the
16

In the BGG framework, a portion of aggregate resources is lost due to the monitoring of entrepreneursthat face bankruptcy in a given period. Again, the importance of this term in practice is of second order,
and, therefore, it is not included in the resource constraint equation.

18

external finance premium.17
Estimating the SW-model with the financial accelerator using Bayesian ML techniques
requires one to specify a minimum of eight structural disturbances. In the absence of thefinancial accelerator, the model described above includes shocks to disembodied technology, intertemporal preferences, government spending, price and wage markups, and thenominal interest rate. With the financial accelerator, the model also includes shocks tothe external finance premium and entrepreneurs’ net worth, for a total of eight structural
shocks. In their original formulation, Smets and Wouters [2007] estimated the model using the seven macroeconomic time-series discussed above, and they allowed for a shock
to investment-specific technology. However, as emphasized by Justiniano, Primiceri, andTambalotti [2008], the investment-specific technology shock may also be viewed as a disturbance to the financial sector. Because we are interested in determining the extent to whichmovements in investment and output may be due to financial factors, we therefore omit theinvestment-specific technology shock and replace it with the shock to entrepreneurial net
worth.
Except for the two financial shocks, our specification of the model uses the same
stochastic representation of structural disturbances as the SW-model.

Specifically, let[ηtb , ηta , ηtg , ηtp , ηtw , ηtr , ηtfd , ηtnw ]′ denote a vector of mutually-uncorrelated i.i.d. shocks. The
structural shocks to preferences (εbt ) and technology (εat ) are assumed to follow stationary
AR(1) processes:
εbt = ρb εbt−1 + ηtb ;εat = ρa εat−1 + ηta ,whereas the government spending shock (εgt ) follows a stationary AR(1) process that responds to an exogenous shock ηtg and the shock to technology ηta :εgt = ρg εgt−1 + ηtg + ρga ηta .
The cost-push shocks to the price and wage equations (εpt and εwt ) are parametrized as
17

The secular rise in corporate credit spreads is consistent with the deepening of the markets for corporate
debt—for example, the emergence of the “junk bond” market—which allowed higher-risk firms to tap thelong-term cash market. The upward trend in credit spreads is also consistent with the secular increase in
idiosyncratic volatility over the last four decades documented by Campbell, Lettau, Malkiel, and Xu [2001].
To ensure that our estimates of this secular trend were not driven by the outsize widening of credit spreads
since the second half of 2008, we estimated the trend using an outlier-resistant M -regression.

19

stationary ARMA(1,1) processes:pεpt = ρp εpt−1 + ηtp − µp ηt−1
;

w
w
εw
= ρ w εwtt−1 + ηt − µw ηt−1 .
nwRegarding shocks to the financial sector, we assume that both financial shocks (εfdt and εt )follow stationary AR(1) processes:fd
εfd= ρfd εfdtt−1 + ηt ;nw
εnw
= ρnw εnwtt−1 + ηt .

The estimation procedure relates observable data on the growth rates of output, consumption, investment, real wages (all in per capita terms), and prices, along with the nominal short-term interest rate and the credit spread to model variables through the following
system of measurement equations:  yt − yt−1γ   ∆ln CONSt  γ   ct − ct−1     ∆ln INV  γ   i − i
t−1 
t 
   t    ∆ln Wt  γ  wt − wt−1 
,





Yt =  = l + 
l
ln
HRS

  

t
t 

    ∆ln Pt  π  πt   







rt
FFt

 r  

st
0
CSt


∆ln GDPtwhereγ = 100(γ − 1);
π = 100(Π∗ − 1);


Π∗ σc
r = 100
γ −1 .
βThis specification allows for a common trend γ that is estimated from the data in
the growth rates of real GDP, consumption, investment, and the real wage, as well as in
the long-run inflation rate of Π∗ . The long-run level of the nominal interest rate is thendetermined by the model-implied real interest rate in the steady state. Because our proxy
for the external finance premium was linearly detrended, we impose a mean of zero on st
in our estimation procedure.
20

The estimation procedure specifies a mix of structural parameters that are calibrated,
and a set of structural parameters that are estimated using Bayesian ML methods. Following
SW, we calibrate the rate of capital depreciation δ, the parameter governing the size of
fixed costs in production φp , the exogenous share of government spending in GDP, thesteady-state wage mark-up λw , and the curvature parameters of the Kimball aggregatorsin the goods and labor markets ǫp and ǫw , respectively; the calibrated values of theseparameters are the same as those in SW. We also calibrate the entrepreneurial survival
probability θ, to the value used by BGG. Finally, we set the steady-state value of theleverage ratio K/N = 1.7, the value that corresponds to the average leverage ratio in the
U.S. nonfinancial corporate sector over the 1973–2008 period (see Figure 2). Estimatedparameters then include the structural parameters pertaining to the the model without
the financial accelerator, namely the vector [γ, α, β, , σc , σl , λ, ξw , ξp , ǫw , ǫp , ιw , ιp , ϕ, ψ]′ , andthe vector of parameters governing the time-series properties of the shock processes—that
is, the persistence parameters [ρa , ρb , ρg , ρw , ρp , ρfd , ρnw , µw , µp ]′ and the vector of standard
deviations of the eight i.i.d. processes [σa , σb , σg , σw , σp , σr , σfd , σnw ]′ .
In addition to the above set of parameters, we also estimate the key parameter thatgovern the strength of the financial accelerator: χ, the elasticity of the external financepremium with respect to leverage. If χ = 0 then, in the absence of shocks to εfd
t , thepremium on external finance st = 0. In this case, there is no feedback from financial
conditions to the real economy and hence no financial accelerator. By estimating thisparameter, our procedure allows the model to determine the degree of financial market
frictions necessary to explain the cyclical dynamics of the U.S. economy over the 1973–2008
period.
The Bayesian ML estimation procedure requires the specification of a prior distribution
for each parameter estimated. For all parameters that are common to the SW-model,
we adopt their priors for estimation purposes. The additional priors correspond to theparameter determining the elasticity of the external finance premium χ and the parameters
governing the time-series properties of the two financial shocks. We set the prior for χ toa Beta distribution, with prior mean equal to the elasticity calibrated by BGG. Following
the convention in SW, we set the prior distribution of the AR coefficients on the financial
shock processes to a Beta distribution with prior mean equal to 0.5; similarly, the priordistribution for the standard deviation of the financial shocks is set to an Inverse Gamma
distribution with prior mean equal to 0.1.

21

3.4
3.4.1

Results
Parameter Estimates

Table 6 contains our assumed prior distributions, along with the selected moments of theposterior distribution obtained from estimation, for the structural parameters of the model,
whereas Table 7 reports the same results for the parameters pertaining to the shock processes. As indicated by the entries in Tables 6, our posterior estimates of the structuralparameters are in most cases similar to those obtained by Smets and Wouters [2007], though
there are some noticeable differences. For example, our estimates for both the degree of
habit formation and the investment adjustment cost are somewhat higher, as is the intertemporal elasticity of substitution. Similar to the results reported by SW, our model estimates
imply only a minor role for price and wage indexation, but they imply greater wage rigidity
as measured by the Calvo wage-setting parameter. Finally, our estimated monetary policy
rule is less responsive to both inflation and the output gap than the estimates obtained bySW, differences that may partly reflect the shorter sample period used in our estimation.
The elasticity of the external finance premium is estimated to be 0.04—somewhat lower
than its prior mean—but nonetheless substantially greater than zero and in the mid-rangeof previous estimates in the literature, a result pointing to a significant role of financial
frictions in the model.
3.4.2

Impulse Responses and Historical Shock Decomposition

To assess the model’s performance, we report impulse response function to monetary andfinancial shocks in Figure 3–5. A contractionary monetary policy shock (Figure 3) causes
a rise in the nominal interest rate and a deceleration in output and investment. Owing
to habit formation and adjustment costs, the peak response in the level of output occurs
about four quarters after the shock. These results are in line with the conventional wisdom
on both the strength of the monetary transmission mechanism as well as the length of theaverage lag of the economy’s the response to monetary policy actions. The tightening ofmonetary policy also causes a decline in asset values and an increase in the external financepremium. Thus, monetary policy works in part by causing an increase in the cost of externalfunds through the deterioration in the strength of borrowers’ balance sheets.
Adverse credit supply shocks (Figure 4) also cause a significant reduction in economic
activity. A one-standard-deviation increase in the shock to the external finance premium(εfdt ) causes an increase of about 25 basis points in the credit spread, which depresses thelevel of output by about 15 basis points and the level of investment by 75 basis points.
Again, the responses of both the level of investment and output are hump-shaped with thepeak response occurring eight to 10 quarters after the impact of the financial shock. Shocks

22

to net worth (Figure 5) have more pronounced and longer-lasting effects. A one-standarddeviation decline in net worth (εnwt ) implies substantial fluctuations in the levels of output
and investment at the two- to three-year horizon. The long-lasting decline in investment
implies a significant reduction in the supply of capital, which leads to rising marginal costs.
As a result, model estimates imply that adverse shocks to net worth are inflationary andtherefore may represent a significant tradeoff between inflation and output for the monetary
authority.
To assess the relevance of disturbances in financial markets for economic outcomes, Figures 6–9 report the historical shock decomposition implied by the model. These figuresplot the time-path of output, investment, the nominal interest rate, and the credit spread
implied by each of the model shocks. Overall, these results accord well with the historical narrative regarding the role of monetary policy and financial conditions in determining
economic outcomes. Monetary policy is estimated to be expansionary relative to the estimated rule during the 1975–79 period and again during the 2004–05 period. Monetary
policy was significantly contractionary during the 1979–1982 period, the so-called “Volcker
deflation”—and prior to the 1990–91 recession.
According to our shock decomposition, a significant fraction of investment and output
dynamics are determined by shocks that emanate from the financial sector. In particular,
financial disturbances account for nearly all of the investment declines in each recession thathas occurred over the 1973–2008 period. Interestingly, financial shocks had a positive effecton investment during the 1980–82 economic downturn, a finding that is consistent with theview that financial deregulation during this time period helped to spur economic activity.
Regarding the current financial crisis, our model estimates imply that the direction offinancial shocks reversed sharply in 2006, that is, from having a significantly expansionary
effect on investment during 2005 to having a negative influence on investment spending by
mid-2006. From this point onwards, deteriorating financial conditions have caused further
declines in investment spending, culminating in the 10 percent drop in investment activity
observed during the last quarter of 2008. This collapse in investment activity explains about
two percentage points of the measured drop in output growth during 2008:Q4. According
to our estimates, this adverse effect was partially offset by a positive shock to government
spending, which may also proxy for the strength in the net export activity during thisperiod. The remaining drop in output during this last quarter is explained by a drop in
consumption, owing to a sharp increase in the desire of households to save rather thanspend.

23

4

Conclusion

This paper provides compelling evidence of a systematic relationship between financial factors and the macroeconomy. Medium-risk, long-maturity corporate credit spreads are highly
informative about the future course of economic activity. When used in an estimated DSGE
framework, these credit spreads imply that a substantial fraction of cyclical fluctuations in
output and investment over the 1973–2008 period can be attributed to disturbances thatoriginated in the financial sector. Overall, our estimates imply that a New Keynesian DSGE
model with the financial accelerator—when estimated using both real and financial market
data—does 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 three decades and a half.

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28

AppendicesA

Steady State of the Model

The following relations are derived from the steady state of the model without the financial
accelerator:
1 σc
γ − (1 − δ) ;β"#αα (1 − α)(1−α)=;φp (R∗K )α

R∗K =

(A.1)

W

(A.2)

i
k
l
k
k
y
iy
cy
zy
W∗h L∗
C∗= γ − (1 − δ) ;

1 − α R∗K=;αW
 (α−1)l= φp;k  
i
k
i
=
≡
;
y
k
y
i
c
=1−g− ;
≡
y
y
 
k
z
= R∗K
≡
;
y
y
 
1 (1 − α) z  y 
=
.
φw
α
y
c

(A.3)
(A.4)
(A.5)
(A.6)
(A.7)
(A.8)
(A.9)

In the model without financial frictions, the required return on capital in steady state isgiven by
1
R∗K = γ σc − (1 − δ) .
β
The addition of financial market frictions implies that
 χ
1 σc
K
K
γ − (1 − δ) ;
R∗ =
N
β

χ > 0,and the steady-state equations A.1–A.9 are modified accordingly.

29

Table 1: Summary Statistics of Bond Characteristics
Bond Characteristic

Mean

SD

Min

P50

Max

# of bonds per firm/month
Mkt. Value of Issuea ($mil.)Maturity at Issue (years)
Term to Maturity (years)
Duration (years)
S&P Credit Rating
Coupon Rate (pct.)
Nominal Effective Yield (pct.)
Credit Spreadb (bps.)

5.77
290.7
14.0
11.8
6.25
7.77
8.26
178

8.84
306.7
9.4
8.61
3.14
2.19
3.37
275

1.00
1.11
1.0
0.01
0.00
D
0.00
1.20
10

2.00
218.2
10.0
8.62
6.00
A3
7.50
7.76
110

75.0
6,657
50.0
30.0
24.4
AAA
17.5
57.4
4,995

Panel Dimensions
Obs. = 360, 996 N = 5, 269 bonds
Min. Tenure = 1 Median Tenure = 59 Max. Tenure = 294
Note: Sample period: Monthly data from January 1973 to December 2008 for asample of 926 nonfinancial firms. Sample statistics are based on trimmed data (see text
for details).
aMarket value of the outstanding issue deflated by the CPI (1982-84 = 100).
b
Measured relative to comparable maturity Treasury yield (see text for details).

30

Table 2: Financial Asset Prices and Labor Market and Production Indicators
Activity Indicator : Private Nonfarm Payroll Employment
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.407(0.148)-0.258(0.129)--0.273(0.161)-0.233(0.128)---0.422(0.158)-0.256(0.131)-0.123(0.539)
-

0.508

0.505

-1.247(0.459)
0.554

(1)

(2)

(3)

0.307
(0.088)0.098(0.066)-0.263(0.091)
0.090(0.057)--0.283(0.086)0.098(0.057)-0.135(0.233)
-

0.434

0.434

Activity Indicator : Unemployment Rate
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

0.461(0.219)0.461

Activity Indicator : Manufacturing Industrial Production
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-1.207(0.425)-0.559(0.220)--0.982(0.468)-0.512(0.231)---1.117(0.395)-0.560(0.225)-0.514(1.268)
-

0.358

0.356

-2.453(1.062)
0.393

Note: Sample period: 1973:Q1 to 2008:Q4 (T = 140). Dependent variable is ∆4 Yt+4 , whereYt denotes an indicator of economic activity (in logarithms if necessary). Each specification also
includes a constant, current, and three lags of ∆Yt and is estimated by OLS. Heteroscedasticityand autocorrelation-consistent asymptotic standard errors are computed according to Newey andWest [1987] and are reported in parentheses (see text for details).

31

Table 3: Financial Asset Prices, Output, and Investment
Activity Indicator : Real GDP
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.625(0.215)-0.155(0.129)--0.564(0.228)-0.144(0.131)---0.607(0.210)-0.154(0.131)0.102(0.467)
-

0.302

0.297

-0.675(0.478)0.313

Activity Indicator : Real Business Fixed Investment
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.656(0.606)-1.012(0.353)-0.062(0.673)-0.877(0.375)---0.830(0.689)-1.003(0.351)-1.531(2.017)
-

0.371

0.375

-6.094(2.039)0.472

Note: Sample period: 1973:Q1 to 2008:Q4 (T = 140). Dependent variable is ∆4 Yt+4 , whereYt denotes an indicator of economic activity (in logarithms). Each specification also includes
a constant, current, and three lags of ∆Yt and is estimated by OLS. Heteroscedasticity- andautocorrelation-consistent asymptotic standard errors are computed according to Newey and West
[1987] and are reported in parentheses (see text for details).

32

Table 4: Financial Asset Prices and Labor Market and Production Indicators
Activity Indicator : Private Nonfarm Payroll Employment
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.432(0.132)-0.216(0.118)--0.337(0.129)-0.027(0.123)---0.453(0.142)-0.219(0.117)-0.318(0.453)
-

0.699

0.700

-1.319(0.309)
0.750

(1)

(2)

(3)

0.246
(0.095)0.071(0.061)-0.182(0.086)0.009(0.065)--0.250(0.094)0.080(0.066)0.137(0.243)
-

0.492

0.491

Activity Indicator : Unemployment Rate
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

0.600(0.177)
0.553

Activity Indicator : Manufacturing Industrial Production
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.951(0.490)-0.062(0.325)--0.611(0.388)0.852(0.370)---1.127(0.538)-0.113(0.339)-1.700(1.341)
-

0.215

0.237

-6.518(1.209)0.471

Note: Sample period: 1986:Q1 to 2008:Q4 T = 88. Dependent variable is ∆4 Yt+4 , whereYt denotes an indicator of economic activity (in logarithms if necessary). Each specification also
includes a constant, current, and three lags of ∆Yt and is estimated by OLS. Heteroscedasticityand autocorrelation-consistent asymptotic standard errors are computed according to Newey andWest [1987] and are reported in parentheses (see text for details).

33

Table 5: Financial Asset Prices, Output, and Investment
Activity Indicator : Real GDP
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-0.312(0.175)-0.008(0.149)--0.161(0.203)0.226(0.177)---0.377(0.200)-0.018(0.150)-0.198(0.491)
-

0.109

0.101

-2.001(0.407)
0.319

Activity Indicator : Real Business Fixed Investment
Financial Indicator
Term Spread (3-month − 10-year)
Real Federal Funds Rate
Baa Credit Spread
Medium-Risk Long-Maturity Credit Spread
Adj. R2

(1)

(2)

(3)

-1.393(0.636)-0.654(0.400)-0.215(0.729)0.341(0.463)---1.243(0.485)-1.003(0.432)-5.241(2.757)
-

0.431

0.508

-10.21(2.127)0.633

Note: Sample period: 1986:Q1 to 2008:Q4 T = 88. Dependent variable is ∆4 Yt+4 , whereYt denotes an indicator of economic activity (in logarithms). Each specification also includes
a constant, current, and three lags of ∆Yt and is estimated by OLS. Heteroscedasticity- andautocorrelation-consistent asymptotic standard errors are computed according to Newey and West
[1987] and are reported in parentheses (see text for details).

34

Table 6: Prior and Posterior Distributions of Structural Parameters
Prior

PosteriorParameter

Distribution

Mean

SD

Mode

Mean

P5

P95

χ
ϕ
σc
λ
ξw
σl
ξp
ιw
ιp
ψ
φp
rπ
ρ
ry
r∆y
π
100(β −1 − 1)
l
γ
α

Beta
NormalNormalBeta
Beta
Normal
Beta
Beta
Beta
Beta
NormalNormalBeta
NormalNormalGamma
Gamma
NormalNormalNormal

0.07
4.00
1.50
0.70
0.50
2.00
0.50
0.50
0.50
0.50
1.25
1.50
0.75
0.12
0.12
0.62
0.25
0.00
0.40
0.30

0.02
1.50
0.37
0.10
0.10
0.75
0.10
0.15
0.15
0.15
0.12
0.25
0.10
0.05
0.05
0.10
0.10
2.00
0.10
0.05

0.04
6.53
1.10
0.79
0.94
2.26
0.76
0.26
0.22
0.34
1.62
1.12
0.73
0.01
0.11
0.65
0.80
-1.06
0.42
0.26

0.04
6.75
1.11
0.81
0.93
2.41
0.74
0.23
0.23
0.30
1.65
1.12
0.72
0.01
0.09
0.68
0.76
-0.85
0.43
0.26

0.03
5.45
1.00
0.77
0.92
1.90
0.70
0.12
0.14
0.20
1.58
1.05
0.67
0.00
0.07
0.58
0.69
-2.27
0.41
0.22

0.05
7.83
1.21
0.85
0.95
2.86
0.78
0.36
0.40
0.39
1.71
1.20
0.76
0.01
0.13
0.76
0.84
0.74
0.45
0.30

Note: Entries under the heading Priors specify the mean and the standard deviation of
the prior distribution for the model’s estimated structural parameters (see text for details).
Entries under the heading Posterior denote the Bayesian ML estimates of the mean and themode, along with the 5th (P5) and 95th percentiles (P95), of the posterior distribution.

35

Table 7: Prior and Posterior Distributions of Structural Shock Processes
Prior

PosteriorParameter

Distribution

Mean

SD

Mode

Mean

P5

P95

σnw
σfd
σa
σb
σg
σr
σp
σw
ρnw
ρfd
ρa
ρb
ρg
ρr
ρp
ρw
µp
µw
ρga

Inv. Gamma
Inv. Gamma
Inv. Gamma
Inv. Gamma
Inv. Gamma
Inv. Gamma
Inv. Gamma
Inv. Gamma
Beta
Beta
Beta
Beta
Beta
Beta
Beta
Beta
Beta
Beta
Beta

0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50

2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20

0.87
0.06
0.41
0.22
0.49
0.24
0.14
0.24
0.98
0.90
0.93
0.22
0.99
0.22
0.76
0.91
0.55
0.89
0.59

0.86
0.06
0.42
0.22
0.50
0.25
0.14
0.23
0.97
0.88
0.93
0.23
0.99
0.28
0.72
0.90
0.48
0.86
0.66

0.68
0.06
0.38
0.18
0.45
0.22
0.11
0.20
0.95
0.84
0.89
0.08
0.99
0.16
0.61
0.85
0.33
0.79
0.54

1.05
0.07
0.46
0.26
0.55
0.27
0.16
0.26
0.99
0.92
0.96
0.34
0.99
0.40
0.83
0.95
0.67
0.93
0.80

Note: Entries under the heading Priors specify the mean and the standard deviation of the prior distribution for the parameters governing the time-series properties of themodel’s structural shocks (see text for details). Entries under the heading Posterior denotethe Bayesian ML estimates of the mean and the mode, along with the 5th (P5) and 95th
percentiles (P95), of the posterior distribution.

36

Figure 1: Selected Corporate Bond Spreads
Basis points

NBER
Peak

Quarterly

800
Q4.

Avg. Senior Unsecured
Medium-Risk Long-Maturity
Baa
600

400

200

0
1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Note: The black line depicts the average credit spread for our sample of 5,269 senior unsecured
corporate bonds; the red line depicts the average credit spread associated with very long maturity
corporate bonds issued by firms with low to medium probability of default (see text for details);
and the blue line depicts the standard Baa credit spread, measured relative to the 10-year Treasury
yield. The shaded vertical bars denote NBER-dated recessions.

37

Figure 2: Leverage Ratio
Ratio
NBER
Peak

Quarterly

3.0
Q4

2.5

2.0

1.5

1.0
1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Note: The black line depicts the time-series of the cross-sectional averages of the leverage ratiofor U.S. nonfinancial corporations. Leverage is defined as the ratio of the market-value of the firm’s
total assets (V ) to the market-value of the firm’s common equity (E), where the market-value ofthe firm’s total assets is calculated using the Merton-DD model (see text for details). The shaded
vertical bars denote NBER-dated recessions.

38

Figure 3: Model Responses to a Contractionary Monetary Policy ShockOutputConsumption
Percentage points

Percentage points
0.2

0.1
0.0

0.0

−0.1
−0.2

−0.2

−0.3
−0.4

−0.4
−0.5

−0.6

−0.6
−0.7

0

4

8

12

16

20

24

28

32

36

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Investment

Hours worked
Percentage points

Percentage points
0.2

0.5

0.1

0.0

0.0
−0.5

−0.1

−1.0

−0.2
−0.3

−1.5

−0.4
−2.0
0

4

8

12

16

20

24

28

32

36

−0.5

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Wages

Inflation
Percentage points

Percentage points
0.012
0.010
0.008
0.006
0.004
0.002
0.000
−0.002−0.004−0.006

0.01
0.00
−0.01
−0.02
−0.03
−0.04
−0.05
−0.06
−0.07
−0.08
0

4

8

12

16

20

24

28

32

36

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Federal funds rate

Credit spread
Percentage points

Percentage points
1.2

0.15

1.0
0.10

0.8
0.6

0.05

0.4
0.00

0.2
0.0
0

4

8

12

16

20

24

28

32

36

−0.05

40

0

Quarters after shock

4

8

12

16

20

24

28

32

36

40

Quarters after shock

Note: The red lines in each panel depicts the estimated impulse responses of the model’s variables
to a one-standard-deviation monetary policy shock. The shaded bands denote the 80 percent confidence
intervals.

39

Figure 4: Model Responses to an Adverse Credit Supply ShockOutputConsumption
Percentage points

Percentage points
0.10

0.20
0.15
0.10
0.05
0.00
−0.05
−0.10
−0.15

0.05
0.00
−0.05
−0.10

−0.20
0

4

8

12

16

20

24

28

32

36

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Investment

Hours worked
Percentage points

Percentage points
1.0

0.15

0.5

0.10
0.05

0.0
0.00
−0.5

−0.05

−1.0
0

4

8

12

16

20

24

28

32

36

−0.10

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Wages

Inflation
Percentage points

Percentage points
0.010

0.005
0.000

0.008

−0.0050.006−0.010−0.0150.004−0.0200.002−0.025−0.030
0

4

8

12

16

20

24

28

32

36

0.000

40

0

4

8

Quarters after shock

12

16

20

24

8

12

36

40

Credit spread
Percentage points

432

Quarters after shock

Federal funds rate

0

28

16

20

24

28

32

36

Percentage points
0.06

0.35

0.05

0.30

0.04

0.25

0.03

0.20

0.02

0.15

0.01

0.10

0.00

0.05

−0.01

0.00

40

0

Quarters after shock

4

8

12

16

20

24

28

32

36

40

Quarters after shock

Note: The red lines in each panel depicts the estimated impulse responses of the model’s variables
to a one-standard-deviation credit supply shock. The shaded bands denote the 80 percent confidence
intervals.

40

Figure 5: Model Responses to an Adverse Networth ShockOutputConsumption
Percentage points

Percentage points
1

1
0
−1
−2
−3
−4
−5
−6
−7
0

4

8

12

16

20

24

28

32

36

0
−1
−2
−3
−4
−5
−6

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Investment

Hours worked
Percentage points

Percentage points
10

1

0

0

−10

−1

−20

−2

−30

−3

−40
0

4

8

12

16

20

24

28

32

36

40

0

4

8

Quarters after shock

12

16

20

24

Wages

4

32

36

40

Inflation
Percentage points

028

Quarters after shock

8

12

16

20

24

28

32

36

Percentage points
0.5

0.5

0.0

0.4

−0.5

0.3

−1.0

0.2

−1.5

0.1

−2.0

0.0

40

0

4

8

Quarters after shock

12

16

20

24

28

32

36

40

Quarters after shock

Federal funds rate

Credit spread
Percentage points

Percentage points
1.6

2.0

1.4

1.5

1.2
1.0

1.0

0.5

0.8
0.6

0.0

0.4
0.2

−0.5

0.0
0

4

8

12

16

20

24

28

32

36

40

0

Quarters after shock

4

8

12

16

20

24

28

32

36

40

Quarters after shock

Note: The red lines in each panel depicts the estimated impulse responses of the model’s variables toa one-standard-deviation networth shock. The shaded bands denote the 80 percent confidence intervals.

41

Figure 6: Historical Decomposition of Output Growth
4
3
2
1
0

-3
-4
-5
Monetary

Wage mark-up

Technology

Preference

Financial shock

Price mark-up

Gov't

Data

NOTE: The solid blue line depicts the behavior of actual variables expressed in percentage point deviations from steady state, while the colored portion in each column indicate the estimated contribution of the specific shock.

2009.1

2007.1

2005.1

2003.1

2001.1

1999.1

1997.1

1995.1

1993.1

1991.1

1989.1

1987.1

1985.1

1983.1

1981.1

1979.11977.1

1975.1-2

1973.1-1

Figure 7: Historical Decomposition of Investment Growth
8
6
4
2
0

-6
-8
-10
-12
Monetary

Wage mark-up

Technology

Preference

Financial shock

Price mark-up

Gov't

Data

NOTE: The solid blue line depicts the behavior of actual variables expressed in percentage point deviations from steady state, while the colored portion in each column indicate the estimated contribution of the specific shock.

2009.1

2007.1

2005.1

2003.1

2001.1

1999.1

1997.1

1995.1

1993.1

1991.1

1989.1

1987.1

1985.1

1983.1

1981.1

1979.11977.1

1975.1-4

1973.1-2

Figure 8: Historical Decomposition of the Federal Funds Rate
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

-1.5
Monetary

Wage mark-up

Technology

Preference

Financial shock

Price mark-up

Gov't

Data

NOTE: The solid blue line depicts the behavior of actual variables expressed in percentage point deviations from steady state, while the colored portion in each column indicate the estimated contribution of the specific shock.

2009.1

2007.1

2005.1

2003.1

2001.1

1999.1

1997.1

1995.1

1993.1

1991.1

1989.1

1987.1

1985.1

1983.1

1981.1

1979.11977.1

1975.1-1.0

1973.1-0.5

Figure 9: Historical Decomposition of the Credit Spread
0.8
0.6
0.4
0.2
0.0

-0.4
-0.6
Monetary

Wage mark-up

Technology

Preference

Financial shock

Price mark-up

Gov't

Data

NOTE: The solid blue line depicts the behavior of actual variables expressed in percentage point deviations from steady state, while the colored portion in each column indicate the estimated contribution of the specific shock.

2009.1

2007.1

2005.1

2003.1

2001.1

1999.1

1997.1

1995.1

1993.1

1991.1

1989.1

1987.1

1985.1

1983.1

1981.1

1979.11977.1

1975.1

1973.1-0.2