Full text of Working Papers (Federal Reserve Bank of Chicago) : The Chicago Fed DSGE Model, Working Paper 2012-02

View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

Federal Reserve Bank of Chicago

The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell,
Jonas D.M. Fisher, and Alejandro Justiniano

WP 2012-02

The Chicago Fed DSGE Model∗
Scott A. Brave

Jeffrey R. Campbell

Federal Reserve Bank of Chicago

Scott.A.Brave@chi.frb.org

jcampbell@frbchi.org

Jonas D.M. Fisher

Alejandro Justiniano

Federal Reserve Bank of Chicago

jfisher@frbchi.org

ajustiniano@frbchi.org
August 16, 2012

Abstract
The Chicago Fed dynamic stochastic general equilibrium (DSGE) model is used for
policy analysis and forecasting at the Federal Reserve Bank of Chicago. This article
describes its specification and estimation, its dynamic characteristics and how it is used
to forecast the US economy. In many respects the model resembles other medium scale
New Keynesian frameworks, but there are several features which distinguish it: the
monetary policy rule includes forward guidance, productivity is driven by neutral and
investment specific technical change, multiple price indices identify inflation and there
is a financial accelerator mechanism.

JEL Classification Numbers: E1, E2, E3, E4, E5
Keywords: New Keynesian model, DSGE, forecasting, policy analysis

∗

We thank Charlie Evans and Spencer Krane for many helpful comments. Any views expressed herein are
those of the authors and do not necessarily represent those of the Federal Reserve Bank of Chicago or the
Federal Reserve System.

Introduction

In this paper, we describe the Chicago Fed’s estimated dynamic stochastic general equilibrium
model. This framework yields a history of identified structural shocks, which we apply to
illuminate recent macroeconomic developments. To aid in the understanding of these results,
we follow them with summaries of the model’s structure, the data and methodology employed
for estimation, and the estimated model’s dynamic properties.
In several respects, the Chicago Fed DSGE model resembles many other New Keynesian
frameworks. There is a single representative household that owns all firms and provides
the economy’s labor. Production uses capital, differentiated labor inputs, and differentiated
intermediate goods. The prices of all differentiated inputs are “sticky”, so standard forwardlooking Phillips curves connect wage and price inflation with the marginal rate of substitution
between consumption and leisure and marginal cost, respectively. Other frictions include
investment adjustment costs and habit-based preferences.
There are, however, several features of the model which distinguish it from these frameworks. For instance, in addition to the usual current monetary policy shock in the monetary
policy rule, we account for short-term guidance regarding the future path of the federal funds
rate. A factor structure estimated from federal funds and Eurodollar futures prices is used
to identify both a current policy factor and a forward guidance factor.
Also included in our monetary policy rule is a shock which dominates changes in long-run
expected inflation. We refer to this shock, captured in a shifting intercept in the monetary
policy rule, as the inflation anchor shock, and we discipline its fluctuations with data on
long-term inflation expectations from the Survey of Professional Forecasters.
Another distinguishing feature of the Chicago model is the use of multiple price indices.
Alternative available indices of inflation are decomposed into a single model-based measure of
consumption inflation and idiosyncratic (series specific) disturbances that allow for persistent
deviations from this common component. Estimation uses a factor model with the common
factor derived from the DSGE framework.
The model also incorporates a financial accelerator mechanism. We introduce risk-neutral

entrepreneurs into the New Keynesian framework who purchase capital goods from capital
installers using a mix of internal and external resources. These entrepreneurs optimally
choose their rate of capital utilization and rent the effective capital stock to goods producing
firms. The dependence on internal resources explicity links fluctuations in the external finance
premium, private net worth, and the state of the economy.
To identify parameters governing the financial accelerator, we use multiple credit spreads
and data on borrowing by nonfinancial businesses and households. Consistent with our
definition of investment, which includes consumer durables and residential investment as
well as business fixed investment, we relate the external finance premium to a weighted
average of High Yield corporate bond and Asset-backed security spreads, where the weight
each receives is derived from the shares of nonfinancial business and household debt in private
credit taken from the Flow of Funds. To capture the impact of entrepreneurial leverage on
financial conditions, we rely on the ratio of private credit to nominal GDP.
The remainder of the paper proceeds as follows. The following section describes a recent
forecast derived from the model. Section 3 describes the shocks in the model and how they
have contributed to business cycle fluctuations. Section 4 outlines the specification and
estimation of the model and presents parameter estimates. The last two sections reviews
key information required to understand the model’s dynamics. In Section 5 we describe five
of the model’s key equations and Section 6 reports the impulse response functions for key
variables and the five structural shocks which drive most of the model’s fluctuations.

A Recent Forecast

Constructing forecasts based on this model requires us to assign values to its many parameters. We do so using Bayesian methods to update an uninformative prior with data from
1989:Q2 through 2011:Q4. All of our forecasts condition on the parameters equaling their
values at the resulting posterior’s mode. These parameter values together with the data
yield a posterior distribution of the economy’s state in the final sample quarter. Our point
forecasts run the model forward from the mode of the posterior distribution of the economy’s

Table 1: Model Forecasts Q4 over Q4

Real GDP
Federal Funds Rate
Core PCE Inflation
Consumption
Investment

2011 2012 2013 2014 2015
1.6
2.1
2.6
2.5
2.5
0.1
0.2
0.2
0.4
1.3
1.8
1.3
1.0
1.3
1.5
2.3
1.7
2.1
2.1
2.1
6.9
3.4
2.9
3.0
3.2

state in the final sample quarter assuming all shock innovations are equal to zero from then
on.
To address the unique nature of fluctuations in the recent period, we specify a sample
break in our model that begins in 2008:Q1. At this point, we calibrate three parameters and
re-estimate the parameters governing the decomposition of the current policy and forward
guidance factors on the remaining sample. All other parameters are fixed at their pre-sample
break values. The three parameters we calibrate involve a structural break in the persistence
of the discount shock which affects households’ rate of time preference, the variance of the
inflation anchor shock, and in the output gap coefficient in the monetary policy rule.
Increasing the persistence of the shock to the discount rate captures the idea that deleveraging by households following a financial crisis is unusually slow. Its value in the second half
of our sample period raises its half life from a little over half a year in the pre-crisis sample
to more than three years in the second half of our sample. Similarly, lowering the variance
of the inflation anchor shock reflects the fact that inflation expectations exhibit a downward
trend in the early part of our sample, but have fluctuated considerably less since.
In the second half of our sample period, we also work with a coefficient on the output
gap in our policy rule that is three times larger than its pre-crisis estimate. Our motivation
for doing so is that the FOMC’s policy response to the recent downturn in activity was
more aggressive than in previous recessions in our sample, each of which was moderate by
historical standards. Furthermore, in combination with the above, this assumption increases
the likelihood that the zero lower bound on the federal funds rate is binding at any given
date.
3

Table 1 presents data from 2011 and forecasts for the following four years. The first three
rows correspond to three key macroeconomic observables, Real GDP growth (Q4-over-Q4),
the Federal Funds Rate (Q4 average), and growth of the Core PCE deflator (Q4-over-Q4).
The following rows report forecasts of Q4-over-Q4 growth for two model-defined aggregates
of importance: Consumption of nondurable goods and non housing services and Investment
in durable goods, residential housing, and business equipment and structures.
Figure 1 complements this with quarter-by-quarter data and forecasts of these series
along with the log level of per capita hours worked in the nonfarm business sector. The
plots’ dashed grey lines indicate the series’ long-run values. The economy’s long-run GDP
growth rate – which we identify with potential growth – equals 2.7 percent.
The economy grows just below potential throughout the forecast horizon. Consequently,
per capita hours do not return to their steady-state by the end of 2015. The protracted
weakness in the forecast arises from the model’s spread shock. This shock, which embodies
movements in the external finance premium beyond what is warranted by firms’ balance
sheets, has particularly persistent effects on economic activity.
The forecasted path for core PCE inflation remains in the range of 0.7 to 1.6 percent
throughout the forecast horizon, well below the model’s long-run expected inflation rate of
2.6 percent. Our forecast for mild inflation is explained by a recent negative realization of
the model’s price mark-up shock inferred from incoming Q2 data.
The contractionary forces shaping our forecast have been partially offset by monetary
policy, which in our model captures policy makers’ announcements regarding the path of
the federal funds rate over the next ten quarters. Forward guidance has added about 0.4
percent to four quarter real GDP growth over the last year. The forward guidance factor has
supported consumption and investment growth, as well as hours.
Our forecast for the federal funds rate is informed by futures prices which hold the funds
rate in the range of zero to 0.25 percent through the end of 2014. Thereafter, the forecast
rate begins to rise as the conventional monetary policy rule dynamics take over, increasing
to 1.3 percent by the end of 2015. The expected output and inflation gaps are weak enough
to merit only the gradual removal of policy accommodation. The increase in the funds rate
4

Figure
starting
2012Q3
Figure1:1:Forecasts
Quarterly Model
Forecasts
Real GDP

Consumption
3

3
2.5
2.5

2
1.5

2
1
1.5

0.5
2012

2013

2014

2015

2012

Federal Funds Rate

2013

2014

2015

Investment
10

6
5

4
6

3
2

1
2

0
2012

2013

2014

2015

2012

2013

PCE Core

2014

2015

Hours

2.5

-2
-4

2
-6
1.5

-8

-10

0.5

-12
2012

2013

2014

2015

2012

2013

2014

2015

in 2015 instead largely reflects mean reversion in our estimated interest rate rule.

Shock Decompositions

Our analysis identifies the structural shocks responsible for past fluctuations. In total, the
model features eleven structural shocks and sixteen idiosyncratic disturbances without structural interpretations. For parsimony’s sake, we group the shocks according to the following
taxonomy.
Demand.

These are the structural non-policy shocks that move output and consumption-

based inflation in the same direction. The model features four of them. One changes the
households’ rate of time discount. We call this the Discount shock. The next two are financial disturbances. The Spread shock generates fluctuations in the external finance premium
beyond the level warranted by current economic conditions, and the Net Worth shock generates exogenous fluctuations in private balance sheets. Finally, this category also includes a
shock to the sum of government expenditures, net exports, and changes in the valuation of
inventories.
Supply.

Five shocks move real GDP and consumption-based inflation in opposite

directions on impact. These supply shocks directly change
• Neutral Technology,
• Investment-Specific/Capital-Embodied Technology,
• Markups of Intermediate Goods Producers,
• Markups of Labor Unions, and
• Households’ Disutility from Labor
The shock to households’ disutility from labor is assumed to follow an ARMA(1,1) process,
which is a parsimonious way of addressing low frequency movements in per capita hours
worked and high frequency variation in wages.
Policy.

The model’s monetary policy follows an exogenous rule with interest-rate

smoothing, a time varying intercept, and a factor structure which identifies a Current Policy
factor and a Forward Guidance factor. The time varying intercept, or Inflation Anchor
6

shock, is disciplined by equating model-based average expected consumer price inflation to a
measure of long-term inflation expectations taken from the Survey of Professional Forecasters.
The Current Policy shock and Forward Guidance factor are derived from contemporaneous
federal funds futures prices zero to four quarters before they affect the federal funds rate. In
the second half of the sample, we extend the number of futures contracts so as to capture
developments which affect the federal funds rate up to ten quarters ahead.
Residual.

We group the remaining shocks into a residual category. These include the

idiosyncratic, that is series specific, shocks to the various price measures and monetary policy
signals based on their factor structures, as well as the measurement errors in the interest rate
spread and private credit-to-GDP ratio we use to capture the external finance premium and
entrepreneurial net worth.
Table 2 reports the fraction of business-cycle variance attributable to shocks in each category for five key variables, the level of Real GDP, Real Consumption, and Real Investment,
and the Federal Funds Rate and Core PCE Inflation. This decomposition is based on onestep-ahead forecast errors. As already mentioned, we introduce an unanticipated sample
break in 2008:Q1 and hence report decompositions for both sub-samples. Demand shocks
dominate business cycles. This is particulary true in the second half of our sample. Monetary policy shocks make only a minor contribution in the earlier sample period, but explain
almost one-third of GDP’s total business cycle variance in the later period, due largely to
their effect on Investment.
Inflation fluctuations are dominated by supply shocks in the early part of the sample,
with exogenous shocks to intermediate goods’ markups almost entirely accounting for supply
shocks’ 63 percent contribution. In contrast, supply shocks account for between 7 and 12 percent of GDP’s total business-cycle variance depending on the sample period. The accounting
for the Federal Funds Rate’s variance is also very sample-dependent. In the second half of
the sample, demand shocks are the key driver, while policy shocks dominate in the earlier
period. Perhaps this is unsurprising, considering that we classify the shock that directly
moves households’ rate of time preference as “demand,” and increase the activity coefficient
in our interest rate rule post-2007.
7

Table 2: The Model’s Decomposition of Business-Cycle Variance
1989:Q2-2007:Q4
Demand Supply Policy Residual
Real GDP
0.73
0.12
0.12
0.02
Federal Funds Rate
0.20
0.04
0.77
0.00
PCE Core
0.15
0.63
0.13
0.09
Consumption
0.88
0.08
0.03
0.01
Investment
0.88
0.04
0.08
0.00
2008:Q1-2011:Q4
Demand Supply Policy Residual
Real GDP
0.62
0.07
0.31
0.01
Federal Funds Rate
0.78
0.01
0.21
0.00
PCE Core
0.95
0.03
0.01
0.01
Consumption
0.96
0.02
0.03
0.00
Investment
0.61
0.04
0.34
0.00
Note: For each variable, the table lists the fraction of variance at frequencies between 6 and
32 quarters attributable to shocks in the listed categories. The numbers may not add to one
due to rounding.

The Model’s Specification and Estimation

Our empirical work uses eighteen variables, measured from 1989:Q2 through the present:
• Growth of nominal per capita GDP,
• Growth of nominal per capita consumption, which sums Personal Consumption Expenditures on Nondurable Goods and Services;
• Growth of nominal per capita investment; which sums Business Fixed Investment,
Residential Investment, and Personal Consumption Expenditures on Durable Goods
• Per capita hours worked in Nonfarm Business,
• Growth of nominal compensation per hour worked in Nonfarm Business,
• Growth of the implicit deflator for GDP,
• Growth of the implicit deflator for consumption, as defined above,

• Growth of the implicit deflator for investment, as defined above,
• Growth of the implicit deflator for core PCE,
• Growth of the implicit deflator for core CPI,
• The interest rate on Federal Funds,
• Ten-year ahead CPI forecasts from the Survey of Professional Forecasters,
• A weighted average of High-Yield corporate and Mortgage-backed bond spreads with
the 10-year Treasury and an Asset-backed bond spread with the 5-year Treasury; where
the weights equal the shares of nonfinancial business, household mortgage, and household consumer debt in private credit,
• Ratio of private credit-to-GDP; which sums household and nonfinancial business credit
market debt outstanding and divides by nominal GDP,
• Quarterly averages of federal funds and Eurodollar futures contract rates one through
four quarters ahead.
The ratio of private credit-to-GDP is detrended using the Hodrick-Prescott filter with
smoothing parameter 1e5. We do not directly use data on government spending, net exports,
or the change in the valuation of inventories. Their sum serves as a residual in the national
income accounting identity. To construct series measured per capita, we used the civilian
non-institutional population 16 years and older. To eliminate level shifts associated with the
decennial census, we project that series onto a fourth-order polynomial in time.
Our model confronts these data within the arena of a standard linear state-space model.
Given a vector of parameter values, θ, log-linearized equilibrium conditions yield a first-order
autoregression for the vector of model state variables, ζt .
ζt = F (θ)ζt−1 + εt
εt ∼ N (0, Σ(θ))

Here, εt is a vector-valued innovation built from the model innovations described above.
Many of its elements identically equal zero. Table 3 lists the elements of ζt . Habit puts
lagged nondurable consumption into the list, and investment adjustment costs place lagged
investment there. Rules for indexing prices and wages that cannot adjust freely require the
state to include lags of inflation and technology growth. Financial frictions place lagged
entrepreneurial borrowing and net worth in the state. The list includes the lagged policy
rate because it appears in the monetary policy rule.
Gather the date t values of the fourteen observable variables into the vector yt . The model
analogues to its elements can be calculated as linear functions of ζt and ζt−1 . We suppose
that the data equal these model series plus a vector of “errors” vt .
yt = G(θ)ζt + H(θ)ζt−1 + vt
vt = Λ(ϕ)vt−1 + et
et ∼ N (0, D(ϕ))
Here, the vector ϕ parameterizes the stochastic process for vt . In our application, the only
non-zero elements of vt correspond to the observation equations for the three consumptionbased measures of inflation, the GDP deflator, and the spread and private credit-to-GDP
measures. The idiosyncratic disturbances in inflation fit the high-frequency fluctuations in
prices and thereby allow the price markup shocks to fluctuate more persistently. These errors
evolve independently of each other. In this sense, we follow Boivin and Giannoni (2006)
by making the model errors “idiosyncratic”. The other notable feature of the observation
equations concerns the GDP deflator. We model its growth as a share-weighted average of
the model’s consumption and investment deflators.
Table 4 displays the estimated modes for a number of model parameters. We denote
the sample of all data observed with Y and the parameters governing data generation with
Θ = (θ, ϕ). The prior density for Θ is Π(Θ), which resembles that employed by Justiniano, Primiceri, and Tambalotti (2011). Given Θ and a prior distribution for ζ0 , we can use
the model solution and the observation equations to calculate the conditional density of Y ,

Table 3: Model State Variables
Symbol
Ct−1
It−1
p
πt−1

Description
Lagged Consumption
Lagged Investment
Lagged Price Inflation

Kt
At
at
at−1

Stock of Installed Capital
Hicks-Neutral Technology
Growth rate of At
Lagged Growth Rate of At

Zt
zt
zt−1

Investment-Specific Technology
Growth rate of Zt
Lagged Growth Rate of Zt

φt
bt
λw,t

Labor-Supply Shock
Discount Rate Shock
Employment Aggregator’s
Time-varying Wage Markups
Elasticity of Substitution
Intermediate Good Aggregator’s
Time-varying Price Markups
Elasticity of Substitution
Entrepreneurial Borrowing
Need for external finance
Lagged Borrowing
Entrepreneurial Net Worth
Risk-neutral entrepreneurs
Lagged Net Worth
Spread Shock
Net Worth Shock
Government Spending Share Shock
Lagged Nominal Interest Rate
Interest-rate Smoothing
Monetary Policy Shock
Inflation Drift Shock

λp,t
Bt
Bt−1
Nt
Nt−1
νt
ςt
gt
Rt−1
εR,t
πt?

Disappears without
Habit-based Preferences
Investment Adjustment Costs
Indexing “stuck” prices
to lagged inflation

Autoregressive growth of At
Indexing “stuck” wages
to lagged labor productivity growth
Autoregressive growth of Zt
Indexing “stuck” wages
to lagged labor productivity growth

Table 4: Selected Model Parameter Modes
Parameter
ρπ
ρR
φp
φy
α
δ
ιp
ιw
γ?100
γµ100
H
λp
π ss
β
G ss
ν
κp
κw
χ
S
B
N

FKN
τ
ζ
ρb
ρυ
ρς
ρg
ρz
ρµ
ρλp
ρψ
θψ

Description
Mode
Inflation anchor persistence
0.99
Inflation rate smoothing
0.85
Inflation gap response
1.35
Output gap response
0.10
Capital Share
0.17
Depreciation rate
0.03
Indexation Prices
0.08
Indexation Wages
0.28
Steady state consumption growth
0.47
Steady state investment-specific technology growth 0.60
Habit
0.89
Steady state price markup
0.10
Steady state quarterly inflation
0.65
Steady state discount factor
0.997
Steady state residual expenditure share in GDP
0.22
Inverse Frisch elasticity
2.17
Price Phillip’s curve slope
0.001
Wage Phillip’s curve slope
0.005
Utilization elasticity
4.80
Investment adjustment elasticity
7.84
Steady state borrowing to net worth ratio
1.11
Steady state spread
0.69
Net worth elasticity
0.002
Entrepreneur survival probability
0.91
Discount factor persistence
0.76
Spread persistence
0.99
Net worth persistence
0.64
G + NX persistnce
0.99
Neutral technology growth persistence
0.10
Investment technology growth persistence
0.73
Price markup persistence
0.61
AR coefficient labor disutility
0.95
MA coefficient labor disutility
0.98

F (Y |Θ). To form the prior density of ζ0 , we apply the Kalman filter. The actual estimation begins with 1989:Q2. Bayes rule then yields the posterior density up to a factor of
proportionality.
P (Θ|Y ) ∝ F (Y |Θ)Π(Θ)
Beginning in 2008:Q1, we set the persistence of the discount shock at 0.95 and scale the
variance of the inflation anchor shock to be one quarter and the coefficient on the output
gap in the monetary policy rule to be three times their earlier values. We re-estimate the
volatility and factor loadings of the current policy and forward guidance factors and the
standard deviations of the idiosyncratic shocks as well as the volatility of the discount shock.
All remaining parameters are held fixed at their values in the first sub-sample. The Kalman
filter is initialized with the necessary pre-sample data, and estimation on this second sample
period proceeds as in the first except that as noted above we include signals up to ten quarters
ahead in the estimation of the policy rule. We then calculate our forecasts with the model’s
parameter values set to this posterior distribution’s mode.
Table 5 displays the estimate modes for both sample periods for the model parameters
that are re-estimated on the second sub-sample.

Five Key Equations

This section summarizes the inferred parameters by reporting the estimates of five key equations: the two equations of the financial accelerator capturing the External Finance Premium
and the evolution of private Net Worth, and the log-linearized forms of the Monetary Policy
Rule, the Price Phillips Curve, and the Wage Phillips Curve.

5.1

Financial Accelerator

Financial frictions in the model arise from imperfections in private financial intermediation
due to lenders’ costly state verification of the returns realized by entrepreneurs’ projects. We
introduce risk neutral entrepreneurs into the model who at the end of period t purchase capital
goods, Kt , from the capital installers at the price Qt , using a mix of internal and external
13

Table 5: Selected Modes for Re-estimated Parameters
Parameter
σb
σf 1
σf 2
σu1
σu2
σu3
σu4
σu5
σu6
σu7
σu8
σu9
σu10
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10

Description
First Mode
Std. dev. Discount factor shock
0.14
Std. dev. Current Policy factor
0.04
Std. dev. Forward Guidance factor
0.06
Std. dev. 1st idiosyncratic shock
0.04
Std. dev. 2nd idiosyncratic shock
0.02
Std. dev. 3rd idiosyncratic shock
0.02
Std. dev. 4th idiosyncratic shock
0.05
Std. dev. 5th idiosyncratic shock
Std. dev. 6th idiosyncratic shock
Std. dev. 7th idiosyncratic shock
Std. dev. 8th idiosyncratic shock
Std. dev. 9th idiosyncratic shock
Std. dev. 10th idiosyncratic shock
Current 1
1.25
Current 2
0.69
Current 3
0.42
Current 4
-0.21
Current 5
Current 6
Current 7
Current 8
Current 9
Current 10
Lead 1
0.80
Lead 2
1.00
Lead 3
0.92
Lead 4
0.43
Lead 5
Lead 6
Lead 7
Lead 8
Lead 9
Lead 10

Second Mode
0.06
0.05
0.07
0.05
0.03
0.03
0.03
0.02
0.02
0.02
0.09
0.09
0.09
1.25
0.43
0.18
0.08
-0.01
0.02
0.01
-0.01
-0.00
-0.02
0.16
0.55
0.78
1.03
1.00
1.09
1.03
1.05
0.91
0.98

resources, given by end of period net worth, Nt , and borrowing Bt , such that Qt Kt = Nt +Bt .
In the next period, t + 1, entrepreneurs optimally choose the rate of utilization, ut+1 , and
rent the effective capital stock Kt+1 = ut+1 Kt to the goods producing firms, receiving in
k
. At the end of period t+1 they resell the remaining
return the gross rental rate of capital ωt+1

capital stock, (1 − δ)Kt back to the capital producers at the price Qt+1 .
5.1.1

External Finance Premium

We assume that the external finance premium –the ratio of the equilibrium return to capital
and the expected real interest rate– is an increasing function of the entrepreneurs’ leverage
ratio,

Kt Qt
,
Nt

according to

k
]
Et [1 + rt+1
Kt Qt νt
=F
e
t
Nt
Et [ 1+R
]
πt+1

with Rt the nominal interest rate, πt+1 the gross inflation rate and F (1) = 1, F 0 >
0, F 00 > 0.1 The spread shock, eνt , can be viewed as a disturbance to credit supply, moving
the external finance premium beyond the level dictated by entrepreneurial net worth. We
parameterize the steady state level of FKN as well as its elasticity τ . We estimate the former
to be 2.76 and the latter to be pretty small. The annualized steady state external finance
premium is estimated to be 2.98 percent.
5.1.2

Net Worth

The law of motion for entrepreneurial net worth is given by

k
]Bt−1 + 0.09Γt + ςt
Nt = 0.91 K t−1 Qt−1 [1 + rtk ] − Et−1 [1 + rt−1
where Γt is the transfer from exiting to new entrepreneurs and ςt is a shock to net worth
that can arise for instance from time-varying survival probabilities for entrepreneurs. The
AR(1) laws of motion for the spread and net worth shocks, νt and ςt , are estimated to have
independent autoregressive parameters (0.99, 0.64) and volatilities i=0.23, 0.37.
1

Notice that that if entrepreneurs are self-financed, which we rule out in steady state, F (1) = 1 and there
is no external finance premium.

5.2

Monetary Policy Rule

Rt = 0.85Rt−1 + 0.32 1.34

2
1 X
Et (πt+j ) − πt?
4 j=−1

!
+ 0.11

!!
2
M
X
1 X
Et (x̂t+j )
+
ξt−j,j
4 j=−1
j=0

[1 + λ(1 − L)2 (1 − F )2 ]x̂t = λ(1 − L)2 (1 − F )2 ŷt
ξt,j = Aj ftc + Bj ftF + ut,j
Besides the lagged interest rate, the variables appearing on the right-hand side of our
interest rate rule are an inflation gap, an output gap, and current and future deviations from
the systematic component of the rule. For any variable v, v̂ denotes deviations from steady
state.
The inflation gap is the deviation of a four quarter average of model inflation from the
time-varying inflation drift, or anchor, πt∗ which varies exogenously according to an AR(1)
process. The four quarter moving average of inflation includes both lagged, current, and
future values of inflation. The monetary authority uses the structure of the model to forecast
the future terms.
The inflation drift term can be interpreted in the context of the model as the monetary
authority’s medium-run desired rate of inflation. It is perfectly credible in the sense that we
equate model-based average expected consumer price inflation over the next forty quarters
to the ten-year ahead CPI forecast from the Survey of Professional Forecasters.
We define the output gap as the four-quarter moving average of detrended model output.
Following Curdia, Ferrero, Ng, and Tambalotti (2011), the detrending is model-based where
L and F represent the lag and lead operators and λ is a smoothing parameter that we
estimate to be 9104. The filter above approximates the Hodrick-Prescott filter. While the
methodologies differ, figure 2 demonstrates that our output gap also compares well with the
CBO’s output gap measure from 1989:Q2-2007:Q2.
Holding the economy’s growth rate fixed, the long-run response of Rt to a permanent onepercent increase in inflation is 1.3 percent. Thus, the model satisfies the Taylor principle.
Our estimated coefficient of the output response to our rule is 0.1. We scale this coefficient
by a factor of 3 in the second half of our sample.
16

Figure 2: The Output Gap
Model−based Gap in Policy Rule

−5

−10

1990

1992

1994

1996

1998

2000

2002

2004

2006

Model−based Gap and CBO Gap (standardized)

2
1
0
−1
−2
1990

1992

1994

1996

1998

Model−based

2000

2002

CBO

2004

2006

Monetary policy shocks have a factor structure such that the factors ftc and ftF represent
the i.i.d. current policy shock and the forward guidance factor. The disturbances ut,j are
assumed uncorrelated across both j and t, and the factor structure identified by restricting
the loading matrices, A and B, such that the forward guidance factor only influences future
values of the federal funds rate. Figure 3 depicts our estimates of both factors from 1989:Q22007:Q2.
By including forward looking terms for the inflation and output gaps in the interest rate
rule, we account for news about both up to two quarters ahead from our forward guidance
shocks. We estimate both the current policy and forward guidance factors using contemporaneous data on the federal funds rate and federal funds and Eurodollar futures contract
prices. In the first sub-sample, this includes futures contracts one to four quarters ahead;
while in the second sub-sample, we use futures contracts one to ten quarters ahead.
Historical decompositions highlighting the role played by forward guidance shocks for per
capita GDP, core PCE inflation, and the federal funds rate from 1989:Q2-2007:Q2 are shown
in figures, 4, 5, and 6. Forward guidance played a role in explaining each during the 19931995 and 2002-2004 periods as detailed in Campbell, Fisher, and Justiniano (2012). The
first episode can be linked to statements by Chairman Greenspan extending expectations for
increases in the funds rate, while the second is closely related to the extended period of low
rates that followed 9/11.

5.3

Price Phillips Curve
p
p
πtp = 0.923Et πt+1
+ 0.074πt−1
+ 0.002st + pt

Here, st represents intermediate goods producers’ common marginal cost. The introduction
of inflation drift does not alter the dynamic component of inflation indexation which is linked
to the previous quarter’s inflation rate.
• The slope of the estimated Phillips Curve is considerably flat compared to some other
estimates in the literature. This reflects at least in part our sample period which starts
in 1989.
18

Figure 3: Current Policy and Forward Guidance Factors
Current Policy Factor
0.05

−0.05

−0.1

1990

1992

1994

1996

1998

2000

2002

2004

2006

2004

2006

Forward Guidance Factor
0.1

0.05
0
−0.05
−0.1

1990

1992

1994

1996

1998

2000

2002

Figure 4: Historical Decomposition of per capita GDP
GDP (per capita)
Data
Annualized

6
4
2
0
-2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

Demand
Annualized

5
0
-5

1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

Supply
2
1
0
-1

-2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

FG
1
0
-1

1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

Other Policy
1.5
1
0.5
0
-0.5
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Counter Factual

Figure 5: Historical Decomposition
PCE Core of Core PCE Inflation
Data
Annualized

5
4
3
2

1
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

Demand
Annualized

-0.2
-0.4
-0.6
-0.8
-1
-1.2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

Supply
0
-1
-2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

FG
Annualized

0.15
0.1
0.05
0
-0.05
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

Other Policy
2
1.5
1
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Counter Factual

Figure 6: Historical Decomposition
of the Federal Funds Rate
Federal Funds Rate

Annualized

Data
8
6
4
2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

2001Q2

2003Q2

2005Q2

2007Q2

Demand
Annualized

1
0
-1
-2

1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

Supply

-1
-1.5
-2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Annualized

FG
1
0
-1

-2
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Other Policy
Annualized

3
2
1
0
-1
1989Q2

1991Q2

1993Q2

1995Q2

1997Q2

1999Q2

Counter Factual

• Producers unable to update their price with all current information are allowed to index
their prices to a convex combination of last quarter’s inflation rate with the steadyp
state inflation rate. This places πt−1
in the Phillips curve. The estimated weight on

steady-state inflation is 0.92.

5.4

Wage Phillips Curve

The Wage Phillips curve can be written as

w
p
p
+ πt+1
+ jt+1 − ιw (πtp + jt ) + κw xt + w
+ jt−1 = βEt πt+1
πtw + πtp + jt − ιw πt−1
t ,
where πtw and πtp correspond to inflation in real wages and consumption prices respectively,
α
jt = zt + 1−α
µt is the economy’s technologically determined stochastic trend growth rate, with

α equal to capital’s share in the production function, zt the growth rate of neutral technology,
p
and µt the growth rate of investment-specific technical change. The term πt−1
+ zt−1 + jt

arises from indexation of wages to a weighted average of last quarter’s productivity-adjusted
price inflation and its steady state value. The estimated weight on the steady state equals
0.72. The log-linearized expression for the ratio of the marginal disutility of labor, expressed
in consumption units, to the real wage is
xt = bt + ψt + νlt − λt − wt ,
where bt and ψt are disturbances to the discount factor and the disutility of working, respectively, lt hours, λt the marginal utility of consumption and wt the real wage. Finally, w
t is a
white noise wage markup shock.
Note that without indexation of wages to trend productivity, this equation says that
nominal wage inflation (adjusted by trend growth) depends positively on future nominal wage
inflation (also appropriately trend-adjusted), and increases in the disutility of the labor-real
wage gap.

The estimated equation is given by

p
p
w
πtw + πtp + jt − 0.28 πt−1
+ jt−1 = 0.997 × Et [πt+1
+ πt+1
+ jt+1 − 0.28 (πtp + jt )] + 0.01xt + w
t ,

The Model’s Responses to Key Shocks

The following shocks figure prominently into explaining the structure of the model: The
discount rate shock, the spread shock to the external finance premium, the neutral technology
shock, the price mark-up shock, the monetary policy (current and forward guidance factor)
and inflation anchor shocks. In this section, we provide greater detail on the model’s responses
to these seven shocks by presenting impulse response functions to a one standard deviation
realization of each of these disturbances.
Figure 7 plots responses to a discount rate shock that increases impatience and tilts
desired consumption profiles towards the present. The variables examined are real GDP, the
federal funds rate, consumption, investment, inflation, and hours worked.
In a neoclassical economy, this shock would be contractionary on impact. Upon becoming
more impatient, the representative household would increase consumption and decrease hours
worked. To the extent that the production technology is concave, interest rates and real wages
would rise; and regardless of the production technology both real GDP and investment would
drop.
Increasing impatience instead expands activity in this New Keynesian economy. As in the
neoclassical case, consumption rises on impact. However, investment remains unchanged as
adjustment costs penalize the sharp contraction of investment from the neoclassical model.
Instead, investment displays a hump-shaped response, exhibiting negative co-movement with
consumption with a slight lag. Habit causes the consumption growth to persist for two more
quarters before it begins to decline. Market clearing requires either a rise of the interest rate
(to choke off the desired consumption expansion) or an expansion of GDP. By construction,
the monetary policy rule prevents the interest rate from rising unless the shock is inflationary
or expansionary. Therefore, GDP must rise. This in turn requires hours worked to increase.

Figure 7: ResponsesDiscount
to a Discount Rate Shock
Federal Funds Rate

GDP (level)

0.2
0.6
0.15

0.5

0.1

0.4
0.3

0.05
0

Consumption (level)

Investment (level)

0.8

0.7
0.6

−0.05

0.5
0.4

−0.1

0.3
0

Hours

PCE Core

0.7

0.07

0.6

0.06

0.5

0.05

0.4
0.04
0.3
0

0.03
0

Two model features overcome the neoclassical desire for more leisure. First, some of the
labor variants’ wages are sticky. For those, the household is obligated to supply whatever
hours firms demand. Second, the additional labor demand raises the wages of labor variants
with wage-setting opportunities. This rise in wages pushes marginal cost up and lies behind
the short-run increase in inflation. After inflation has persisted for a few quarters, monetary
policy tightens and real rates rise.
Since the discount rate shock moves output and prices in the same direction, a Keynesian
analysis would label it a shift in “demand.” In the neoclassical sense, it is also a demand
shock, albeit a reduction in the demand for future goods. The matching neoclassical supply
shock in our model is to the spread shock. A positive shock to it decreases the supply of
future goods. Figure 8 plots the responses to such a shock.2
A positive spread shock reduces the supply of credit available to entrepreneurs, who are
then forced to shrink their demand for capital. The price of installed capital drops sharply
so that the return to capital collapses on impact and is followed by a prolonged contraction
in borrowing by entrepreneurs. The decline in borrowing is initially smaller than in net
worth, which results in a rising leverage ratio and a further tightening of the external finance
premium. Investment and other measures of real activity, with the exception of consumption,
all decline. In response to lower activity and inflation, monetary policy eases and real rates
move lower.
Increasing the external finance premium thus lowers investment, hours worked, GDP,
and the real interest rate. Two aspects of our model limit the response of consumption on
the same shock’s impact. First, habit-based preferences penalize an immediate increase in
consumption. Second, monetary policy responds to the shock only slowly, so real interest rates
are slow to adjust. Although this shock changes the economy’s technology for intertemporal
substitution – and therefore deserves the neoclassical label “supply” – it makes prices and
output move in the same direction. For this reason, it falls into our Keynesian taxonomy’s
2

The interpretation of this shock is not unique. The negative spread shock resembles in nature a positive
marginal efficiency of investment (MEI) shock. It could also be interpreted as a shock to the efficiency of
channeling funds to entrepreneurs or, more broadly, variations in the supply of credit. Barro and King (1984)
and Greenwood, Hercowitz, and Huffman (1988) consider the analogous responses to an MEI shock from a
neoclassical model.

Spread
Figure 8: Responses
to a Spread Shock
Federal Funds Rate

GDP (level)
−0.4

−0.1

−0.6
−0.8

−0.2

−1
−1.2

−0.3

−1.4
0

Consumption (level)

Investment (level)
−2

0.4

−4
0.3
−6
0.2

0.1
0

−8

−10
0

Hours

PCE Core
−0.05

−0.4

−0.06
−0.6
−0.07
−0.8
−0.08
−1
0

−0.09
0

“demand” category.
Figure 9 displays the responses to a neutral technology shock. Measures of real activity,
with the exception of hours, all rise after a positive technology shock. The effects are delayed,
however, due to habit persistence in consumption and investment adjustment costs. As
inflation declines on impact, monetary policy progressively eases over a period of 6 quarters
before bringing real rates back to their steady-state as real activity picks up. This results in a
hump-shaped response in GDP, consumption, and investment. Since the neutral technology
shock moves output and prices in opposite directions, we label it a shift in “supply.”
Figure 10 depicts the responses to a positive price mark-up shock. Inflation increases on
impact and measures of real activity all decline, thereby resembling a transitory negative
technology shock. Monetary policy tightens over a period of four quarters before real rates
gradually return to their steady-state as real activity picks up.
Figures 11 and 12 present the impulse response functions for our two monetary policy
shocks, the current policy and forward guidance factors. We begin with the forward guidance
factor. A positive realization of this shock signals a hump-shaped increase in the interest
rate given our estimated factor loadings with limited movement in the rate today. The
gradual decline in the interest rate after four quarters is governed mostly by the autoregressive
coefficient in the rule.
In response to the anticipated tightening, activity contracts immediately, afterward following a hump-shaped response. Inflation declines primarily on impact, as forward looking
price setters incorporate the weaker outlook for activity into their decisions today. The current policy factor displays a similar pattern, except that compared with the forward guidance
factor it accelerates the policy tightening. That is, it displays an immediate jump followed
by a steeper rise and subsequent fall.
The responses to the current policy factor are standard, but those following a forward
guidance shock require more explanation. At the announcement date, the expected value of
the policy rate four quarters hence rises. Because both Phillips curves are forward looking,
this expected contraction causes both prices and quantities to fall. This anticipated weakness
then feeds through the monetary policy rule to create a gradual easing of policy.
28

Figure 9: Responses
to aTechnology
Neutral Technology Shock
Neutral
Federal Funds Rate

GDP (level)
0.7

−0.02

0.6
0.5

−0.04

0.4
0.3

−0.06
0

0.2
5

Consumption (level)

Investment (level)
1.5

0.6
0.5

0.4
0.3
0.2
0.1
0

0.5
5

Hours

5
−3

x 10

PCE Core

0
−0.1

−5

−0.2
−0.3

−10

−0.4
0

Figure 10: Responses
a Price Mark-up Shock
PricetoMarkup
Federal Funds Rate

GDP (level)
−0.02

0.08

−0.03
0.06

−0.04

0.04

−0.05

0.02

−0.06

−0.07
0

Consumption (level)

Investment (level)

−0.01

−0.1

−0.015

−0.15

−0.02

−0.2

−0.025
0

−0.25
5

Hours

PCE Core

−0.02
−0.03

0.3

−0.04

0.2

−0.05
0.1

−0.06
−0.07
0

0
0

Figure 11:Contemporaneous
Responses to the Policy
Current
Policy Factor
Factor
Federal Funds Rate

GDP (level)
−0.1

0.3
0.2

−0.15

0.1
−0.2
0

Consumption (level)

Investment (level)
−0.2

−0.04

−0.3
−0.06

−0.4
−0.5

−0.08

−0.6
−0.7

−0.1
0

Hours

5
−3

x 10

PCE Core

−0.05
−5
−0.1
−10

−0.15
−0.2
0

−15
5

Figure 12: Responses
toGuidance
the Forward
Guidance Factor
Forward
Factor
Federal Funds Rate

GDP (level)

0.4

−0.15

0.3

−0.2

0.2

−0.25

0.1

−0.3

0
0

−0.35
0

Consumption (level)

Investment (level)

−0.06

−0.4

−0.08

−0.6

−0.1
−0.8

−0.12

−1

−0.14
−0.16
0

−1.2
5

Hours

PCE Core

−0.1

−0.005

−0.15

−0.01

−0.2

−0.015

−0.25

−0.02

−0.3
−0.35
0

−0.025
5

Figure 13 displays the impulse response functions for a positive inflation anchor shock.
In response, inflation jumps on impact, as does expected long-run expected inflation (not
shown). Under the assumption of perfect credibility, higher inflation is achieved without
any contemporaneous movement in the federal funds rate. Although monetary policy does
eventually tighten to return the real interest rate to its steady-state, lower real rates during
the initial transition fuel an increase in consumption, investment, and hours. Therefore,
GDP moves up as well. Given the high degree of persistence of this shock, its effects on real
activity and inflation dissipate at a glacial pace.

Figure 13: Responses
to anDrift
Inflation Drift Shock
Inflation
Federal Funds Rate

GDP (level)
0.1

0.1
0.08

0.08
0.06

0.06

0.04

0.02
0

Consumption (level)

Investment (level)

0.04

0.35
0.3

0.03

0.25
0.2

0.02

0.15
0.01
0

0.1
0

Hours

PCE Core
0.12

0.08
0.115
0.06
0.11
0.04
0

0.105
5

References
Barro, R. J. and R. G. King (1984).

Time-separable preferences and intertemporal-

substitution models of business cycles. The Quarterly Journal of Economics 99 (4), pp.
817–839.
Bernanke, B. S., M. Gertler, and S. Gilchrist (1999). The financial accelerator in a quantitative business cycle framework. Handbook of Macroeconomics.
Boivin, J. and M. Giannoni (2006). DSGE models in a data-rich environment. Working
Paper 12772, National Bureau of Economic Research.
Campbell, J., J. Fisher, and A. Justiniano (2012). FOMC forward guidance and the business
cycle. Working Paper, Federal Reserve Bank of Chicago.
Curdia, V., A. Ferrero, G. C. Ng, and A. Tambalotti (2011). Evaluating interest rate rules
in an estimated DSGE model. Working Paper 510, Federal Reserve Bank of New York.
Greenwood, J., Z. Hercowitz, and G. W. Huffman (1988). Investment, capacity utilization,
and the real business cycle. The American Economic Review 78 (3), pp. 402–417.
Justiniano, A., G. E. Primiceri, and A. Tambalotti (2011). Investment shocks and the relative
price of investment. Review of Economic Dynamics 14 (1), 101–121.

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Why Has Home Ownership Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais

WP-09-01

Why do the Elderly Save? The Role of Medical Expenses
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-09-02

Using Stock Returns to Identify Government Spending Shocks
Jonas D.M. Fisher and Ryan Peters

WP-09-03

Stochastic Volatility
Torben G. Andersen and Luca Benzoni

WP-09-04

The Effect of Disability Insurance Receipt on Labor Supply
Eric French and Jae Song

WP-09-05

CEO Overconfidence and Dividend Policy
Sanjay Deshmukh, Anand M. Goel, and Keith M. Howe

WP-09-06

Do Financial Counseling Mandates Improve Mortgage Choice and Performance?
Evidence from a Legislative Experiment
Sumit Agarwal,Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-09-07

Perverse Incentives at the Banks? Evidence from a Natural Experiment
Sumit Agarwal and Faye H. Wang

WP-09-08

Pay for Percentile
Gadi Barlevy and Derek Neal

WP-09-09

The Life and Times of Nicolas Dutot
François R. Velde

WP-09-10

Regulating Two-Sided Markets: An Empirical Investigation
Santiago Carbó Valverde, Sujit Chakravorti, and Francisco Rodriguez Fernandez

WP-09-11

Working Paper Series (continued)
The Case of the Undying Debt
François R. Velde
Paying for Performance: The Education Impacts of a Community College Scholarship
Program for Low-income Adults
Lisa Barrow, Lashawn Richburg-Hayes, Cecilia Elena Rouse, and Thomas Brock
Establishments Dynamics, Vacancies and Unemployment: A Neoclassical Synthesis
Marcelo Veracierto

WP-09-12

WP-09-13

WP-09-14

Working Paper Series (continued)
The Price of Gasoline and the Demand for Fuel Economy:
Evidence from Monthly New Vehicles Sales Data
Thomas Klier and Joshua Linn

WP-09-15

Estimation of a Transformation Model with Truncation,
Interval Observation and Time-Varying Covariates
Bo E. Honoré and Luojia Hu

WP-09-16

Self-Enforcing Trade Agreements: Evidence from Time-Varying Trade Policy
Chad P. Bown and Meredith A. Crowley

WP-09-17

Too much right can make a wrong: Setting the stage for the financial crisis
Richard J. Rosen

WP-09-18

Can Structural Small Open Economy Models Account
for the Influence of Foreign Disturbances?
Alejandro Justiniano and Bruce Preston

WP-09-19

Liquidity Constraints of the Middle Class
Jeffrey R. Campbell and Zvi Hercowitz

WP-09-20

Monetary Policy and Uncertainty in an Empirical Small Open Economy Model
Alejandro Justiniano and Bruce Preston

WP-09-21

Firm boundaries and buyer-supplier match in market transaction:
IT system procurement of U.S. credit unions
Yukako Ono and Junichi Suzuki
Health and the Savings of Insured Versus Uninsured, Working-Age Households in the U.S.
Maude Toussaint-Comeau and Jonathan Hartley

WP-09-22

WP-09-23

The Economics of “Radiator Springs:” Industry Dynamics, Sunk Costs, and
Spatial Demand Shifts
Jeffrey R. Campbell and Thomas N. Hubbard

WP-09-24

On the Relationship between Mobility, Population Growth, and
Capital Spending in the United States
Marco Bassetto and Leslie McGranahan

WP-09-25

The Impact of Rosenwald Schools on Black Achievement
Daniel Aaronson and Bhashkar Mazumder

WP-09-26

Comment on “Letting Different Views about Business Cycles Compete”
Jonas D.M. Fisher

WP-10-01

Macroeconomic Implications of Agglomeration
Morris A. Davis, Jonas D.M. Fisher and Toni M. Whited

WP-10-02

Accounting for non-annuitization
Svetlana Pashchenko

WP-10-03

Working Paper Series (continued)
Robustness and Macroeconomic Policy
Gadi Barlevy

WP-10-04

Benefits of Relationship Banking: Evidence from Consumer Credit Markets
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-10-05

The Effect of Sales Tax Holidays on Household Consumption Patterns
Nathan Marwell and Leslie McGranahan

WP-10-06

Gathering Insights on the Forest from the Trees: A New Metric for Financial Conditions
Scott Brave and R. Andrew Butters

WP-10-07

Identification of Models of the Labor Market
Eric French and Christopher Taber

WP-10-08

Public Pensions and Labor Supply Over the Life Cycle
Eric French and John Jones

WP-10-09

Explaining Asset Pricing Puzzles Associated with the 1987 Market Crash
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-10-10

Prenatal Sex Selection and Girls’ Well‐Being: Evidence from India
Luojia Hu and Analía Schlosser

WP-10-11

Mortgage Choices and Housing Speculation
Gadi Barlevy and Jonas D.M. Fisher

WP-10-12

Did Adhering to the Gold Standard Reduce the Cost of Capital?
Ron Alquist and Benjamin Chabot

WP-10-13

Introduction to the Macroeconomic Dynamics:
Special issues on money, credit, and liquidity
Ed Nosal, Christopher Waller, and Randall Wright

WP-10-14

Summer Workshop on Money, Banking, Payments and Finance: An Overview
Ed Nosal and Randall Wright

WP-10-15

Cognitive Abilities and Household Financial Decision Making
Sumit Agarwal and Bhashkar Mazumder

WP-10-16

Complex Mortgages
Gene Amromin, Jennifer Huang, Clemens Sialm, and Edward Zhong

WP-10-17

The Role of Housing in Labor Reallocation
Morris Davis, Jonas Fisher, and Marcelo Veracierto

WP-10-18

Why Do Banks Reward their Customers to Use their Credit Cards?
Sumit Agarwal, Sujit Chakravorti, and Anna Lunn

WP-10-19

Working Paper Series (continued)
The impact of the originate-to-distribute model on banks
before and during the financial crisis
Richard J. Rosen

WP-10-20

Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, and Nan Yang

WP-10-21

Commodity Money with Frequent Search
Ezra Oberfield and Nicholas Trachter

WP-10-22

Corporate Average Fuel Economy Standards and the Market for New Vehicles
Thomas Klier and Joshua Linn

WP-11-01

The Role of Securitization in Mortgage Renegotiation
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-02

Market-Based Loss Mitigation Practices for Troubled Mortgages
Following the Financial Crisis
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-03

Federal Reserve Policies and Financial Market Conditions During the Crisis
Scott A. Brave and Hesna Genay

WP-11-04

The Financial Labor Supply Accelerator
Jeffrey R. Campbell and Zvi Hercowitz

WP-11-05

Survival and long-run dynamics with heterogeneous beliefs under recursive preferences
Jaroslav Borovička

WP-11-06

A Leverage-based Model of Speculative Bubbles (Revised)
Gadi Barlevy

WP-11-07

Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
Sule Alan, Bo E. Honoré, Luojia Hu, and Søren Leth–Petersen

WP-11-08

Fertility Transitions Along the Extensive and Intensive Margins
Daniel Aaronson, Fabian Lange, and Bhashkar Mazumder

WP-11-09

Black-White Differences in Intergenerational Economic Mobility in the US
Bhashkar Mazumder

WP-11-10

Can Standard Preferences Explain the Prices of Out-of-the-Money S&P 500 Put Options?
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-11-11

Business Networks, Production Chains, and Productivity:
A Theory of Input-Output Architecture
Ezra Oberfield
Equilibrium Bank Runs Revisited
Ed Nosal

WP-11-12

WP-11-13

Working Paper Series (continued)
Are Covered Bonds a Substitute for Mortgage-Backed Securities?
Santiago Carbó-Valverde, Richard J. Rosen, and Francisco Rodríguez-Fernández

WP-11-14

The Cost of Banking Panics in an Age before “Too Big to Fail”
Benjamin Chabot

WP-11-15

Import Protection, Business Cycles, and Exchange Rates:
Evidence from the Great Recession
Chad P. Bown and Meredith A. Crowley

WP-11-16

Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen

WP-12-01

The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano

WP-12-02

Full text of Working Papers (Federal Reserve Bank of Chicago) : The Chicago Fed DSGE Model, Working Paper 2012-02

FRASER