Comments on “Credit Risk and theMacroeconomy: Evidence from an Estimated
DSGE Model” by Gilchrist, Ortiz, andZakrajsek
Todd Clark
Federal Reserve Bank of Kansas CityMay 27, 2009

Broad Goals of this Paper and Antecedents:

Improve measures of financial frictions for forecasting
economic activity
Improve understanding (models) of the role of frictions in theeconomy

Contributions of this Paper
Quantifies the role of the financial accelerator in 1973-2008
U.S. data
Extends authors’ prior work to provide a (longer)
1973-2008 time series on the GOZ spread.
Provides evidence of reduced-form predictive content of
the GOZ spread for economic activity in the longer sample.
Incorporates the GOZ spread in an estimated DSGE
model, as a measure of the external finance premium(EFP).
Model with financial accelerator of Bernanke, Gertler, andGilchrist (1999).
Asymmetric information ⇒ premium on external financing
Accelerator: feedback between net worth and premium

Most studies have treated the EFP as unobserved.
De Graeve (2008) and Christensen and Dib (2008)

Outline

1

Comments on empirical work

2

Comments on DSGE modeling

Comments on Empirical Work: (1) Measurement
The GOZ spread has a high correlation with a broader index offinancial stress. So is the GOZ spread a better measure of theEFP, or a broader correlate of financial stress?
4.0

5

3.5

4

3.0
3
2.5
2
2.0
1
1.5
0

1.0

0.5

-1
1990

1992

1994

1996

GOZ spread (left scale)

1998

2000

2002

2004

2006

2008

Hakkio-Keeton stress index (right scale)Comments on Empirical Work: (2) Stability
Model stability deserves a closer look, but the GOZ spreadlooks relatively good (stable).
Most spreads have proven to be unstable predictors:paper-bill spread (Friedman and Kuttner 1999), termspread (Estrella, et al. (2003))
Break tests considered by the authors generally indicate
the models to be stable.
But these tests often have poor power (Cogley andSargent 2005).
Shouldn’t the term spread make the models unstable?
Let’s reconsider stability using bivariate VARs allowing
time-varying parameters and error variances....
Models use one-quarter growth rates and four lags.

Comments on Empirical work: (2) Stability
Relationship of activity to term spread looks unstable.
TVP-VAR: sums of coefficients, equation for Employment growth
0.95

lags of Employment growthlags of Term spread

0.8

0.90
0.6

0.85

0.80
0.4
0.75

0.70
0.2
0.65

0.60

0.00.55

0.50
1975

1980
median

1985

1990

1995

15%ile2000

2005
85%ile

-0.2
1975

1980
median

1985

1990

1995

15%ile2000

2005
85%ileComments on Empirical Work: (2) Stability
Relationship of activity to GOZ spread looks relatively stable.
TVP-VAR: sums of coefficients, equation for Employment growth
0.80

lags of Employment growthlags of GOZ spread

0.00

-0.25
0.75
-0.50
0.70-0.75

0.65

-1.00

-1.25

0.60

-1.50
0.55
-1.75
0.50
-2.00

0.45
1975

1980
median

1985

1990

1995

15%ile2000

2005
85%ile

-2.25
1975

1980
median

1985

1990

1995

15%ile2000

2005

85%ileComments on Empirical Work: (3) Forecasting

Does the GOZ spread help forecast out of sample?
In-sample predictive content often fails to apply out ofsample, due to overfitting or instabilities.
Prior paper shows GOZ spread to improve out-of-sample
forecasts, but over a very short sample.
OOS results for the longer sample of this paper?
Compare to AR model benchmarks, in light of instabilities
with term spread.

Results for real-time forecasts?
AR model benchmarks
Could the spread have improved forecasts from Greenbook
or the Survey of Professional Forecasters?

Comments on Empirical Work: (3) Forecasting
A preliminary check for a few variables suggests the GOZ
spread does help forecast out of sample.
Forecasts of four-quarter growth rates, 1986:Q3-2008:Q4
Benchmark AR model (3 lags) versus model adding one
lag of GOZ spread
Statistical significance evaluated with new, improved
testing approach of Clark and McCracken (2009).

employment (private)GDPbusiness fixed investment

MSE: AR /MSE: with spread
0.997
1.157
1.176

p-value.287.059.055

Comments on Empirical Work: (3) Forecasting

One outstanding concern: potential impacts of data
mining/specification search
From a range of spreads, the GOZ spread has been
selected to maximize predictive content in the available
sample.
Conventional p-values fail to capture search/maximization
and understate uncertainty.
There is some risk the GOZ spread could not predict aswell in the future.

Comments on DSGE Modeling: (1) General Issues inLiterature
Does the financial accelerator (FA) significantly affect thedynamics of GDP, or just investment?
Useful to show results from model with and without FA.
In Christensen and Dib (2008), the FA affects investment,
but not GDP much.
Is the sensitivity driven by adjustment costs on capitalrather than investment (De Graeve 2008)?
Are results stable over subsamples?
Significant changes over time in shock variances(Primiceri, et al.)Significant changes over time in monetary policy

Comments on DSGE Modeling: (2) Benefits ofIncluding Spread in ModelWhat do we get out of including a measure of the EFP in themodel?
Should the model include measurement error in the EFP?
How does the GOZ spread compare to the model-inferredseries of De Graeve (2008)?
Does the implied response of monetary policy to a credit
supply shock seem plausible?
Does incorporating data on the EFP in the model
estimation yield a better description of macro data?
Is the Bayesian measure of model fit — the marginal
likelihood — higher when the GOZ spread is included thanwhen it is not?
Does the DSGE model forecast better with the GOZ spread
included?

Summary

This is a very useful line of research.
Provides a relatively long time series on a credit spreadwith significant predictive content.
Provides a better assessment of the role of financial
frictions in DSGE models.
Future: could assess optimal monetary policy responses toshocks to credit supply.

Summary

Suggestions for empirical work
Give more consideration to measurement.
Give more consideration to possible instabilities.
Examine out-of-sample forecasting.
Suggestions for DSGE analysis
Consider some of the issues identified by prior work on theFA in DSGE models.
Establish the benefits of including the GOZ spread in themodel estimation.