- Benoit Mojon

Would Macroprudential Policies have
Prevented the Great Recession?
Pam…li Antipay
Eric Mengusz
Benoit Mojonx
(Preliminary and incomplete)
November 30, 2010
Abstract
This paper presents simulations based on a new macroeconomic model of the interplay between housing prices and the business cycle. The model introduces risk shifting
à la Allen and Gale in a Iacoviello economy and two forms of lean against credit policies, either through an augmented Taylor rule or through raising the cost of credit
independently from the short term interest rate. A major di¤erence of the model with
the existing litterature is that public policies can improve welfare when it limits the
externality associated to risk shifting.
We estimate the model for the euro area, the UK and the US on the 1985-2010 period. We show that macroprudential policy would have been very e¤ective in smoothing
the last credit cycle, reducing the depth of the Great Recession. This policy may have
implied, however, an in‡ation rate persistently inferior to 2 %.
Acknoledgements: We would like to thank Jean Barthelemy, Laurent Clerc, Emmanuel Fahri, François
Gourio, Michele Lenza, Magali Marx and Xavier Ragot for comments and suggestions on this research as
well as Xintong Han for excellent research assistance.
y
Banque de France
z
Banque de France and Toulouse School of Economics
x
Banque de France, corresponding author: [email protected]
1
1
Introduction
There is a broad international consensus that the void between microeconomic banking
regulation and monetary policy should be …lled with "Macroprudential policies". These
policies should consist of limiting the occurence and the scale of systemic risk. However, we
have yet very few formal analyses of the impact of macroprudential policies1 . For instance
we need such analyses to help policy makers graps whether and how this new macroeconomic
policy will impact the transmission of monetary policy.
This paper contributes to …gure out the potency of macroprudential policies by trying to
answer two simple questions:
Would these policies have prevented the crisis that led policy makers to set
them up?
What would the costs of such policies involve, in particular with respect to
price stability?
We answer these questions using simulations of a structural, arguably policy invariant
model. This model focuses on the interplay between housing prices and the business cycle,
introducing risk shifting à la Allen and Gale (2000) in a Iacoviello (2005) economy. This
is, at least conceptually, a major twist to the convential introduction of …nancial frictions
in macroeconomic models. In such models, more credit is always welfare improving because
credit constraints due to asymetric information are sub-optimal. Nikolov (2009) for instance
shows that as long as lenders have projects with lower levels of productivity than borrowers,
more credit would improve welfare even at the cost of underpriced …resales externality and
the subsequent cyclical instability. Such models are therefore ill suited to account for excesses
of credit that many consider to be the main cause of the Great Recession.
We instead develop one of the available models of excess credit2 : the model of risk
shifting, proposed by Allen and Gale (2000). The basic intuition of this model is that risky
asset can be overpriced because levered investors expect to exert the option of defaulting on
their debt in the bad state of the world. We frame this model of credit into a medium scale
1
We know of only three quantitative analyses of the e¤ects of macroprudential policies on the business
cycle: Gertler, Kiyotaki and Queralto (2010); Angelini, Panetta and Neri (2010) and Angeloni and Faia
(2009). TBC
2
Alternative models include Caballero and Krishnamurthy (2003), Farhi and Tirole (2010), Lorenzoni
(2008), Brunnermeier and Pedersen (2009). Korinek (2009), OTHERS ?
2
DSGE model, otherwise similar to Iacoviello (2005). Patient households lend to impatient
households and entrepreneurs up to a collateral value constraint. Nominal rigidities take the
form of Calvo. We also introduce an investment speci…c technology shock as in Justiniano
and Primicieri (2008), and many others.
Our model hence departs in two important ways from the one used by Nikolov. First, debt
acrue not only to entrepreneurs with arguably projects with higher growth potential, but
also to fund residential investments of impatient households with no e¤ects on productivity.
Second, we model explicitely risk shifting and the resource transfers due to default. Taking
these into account increase the odds for credit stabilization policies to improve welfare.
We estimate the model for the euro area, the UK and the US on the 1985-2010 period,
following the approach of Smets and Wouters (2003, 2007). We then consider two forms of
lean against credit policies, either through an augmented Taylor rule or through raising the
cost of credit independently from the short-term interest rate.
We show that macroprudential policy would have been very e¤ective in smoothing the
last credit cycle, reducing the depth of the Great Recession. However such policy, whereby
credit growth would have been slower before the crisis, may also have implied an in‡ation
path inferior to 1% for several years.
[TBC with a discussion of related literature]
2
The model
To begin with, we present a simple partial equilibrium model of risk shifting with only two
periods. We describe the main assumptions and characterise the mechanisms at work for
given functional forms. Thereafter we present the complete general equilibrium model.
2.1
Risk shifting
The economy is populated by patient households who save and by borrowers, both normalized
to one.3 Patient households cannot lend directly to borrowers, hence the need of …nancial
intermediairies/banks. Intermediaries are modelled as a continuum of small banks who
gather deposits from patient households and lend them to borrowers. Following the seminal
contribution of Allen and Gale (2000), we assume limited liability for banks : if the losses
3
In the general equilibrium version of the model borowers can be either impatient households or entre-
preneurs.
3
exceed their capital, they will default on deposits. This implies a risk-shifting phenomenon:
banks will take more risks than they would have taken if they had to absorb losses entirely
in the bad states of the world. We further assume that banks are not capitalized so that
they default as soon as their assets is worth less than their debts. This assumption allows
us to reduce the number of possible outcomes for banks to two: either the loan issued by
the bank is paid and the bank does not default either, or the borrower defaults on the loan
and the bank defaults as well. In case the loan succeeds, bank’s pro…ts are redistributed
to patient households who consider them as an additional return for saving. To phrase it
di¤erently, they expect that the return on their savings includes both the return on deposits
and the redistributed pro…ts made by banks.
The risk in our model is the possibility for borrowers to default. In Iacoviello (2005)’s
model, as in Kiyotaki and Moore (1997), no default occurs at equilibrium because patient
households will lend just as much as what they are able to seize in case of default. In our
model, risk-shifting implies more lending than what the bank expects to be able to repossess.
We also assume that the consequences of defaults are shared within large families à
la Lucas (1990)4 . Each family is either patient and lending or impatient and borrowing.
However, the consequences of defaults are shared within families of borrowers and the returns
of funds invested in banks are mutualized within families of patient households. Default is
then a centralized decision and there is no risk-sharing between patient households and
borrowers. A similar assumption is used by Gertler and Karadi (2010) among others in
order to model depositors, bankers and workers at the same time without having keep track
of heterogeneity.
Finally we assume, in addition to the limited liability assumption of the Risk Shi…ting
model, that individual banks do not internalize the e¤ect of excessive lending on the aggregate
probalibity of default. This is one of the keys to understand why …nancial fragility builds
up in the period that precedes …nancial crises.
2.1.1
Banks
The …nancial sector is a continuum of small banks which are both price-takers and probabilitytakers. Each bank lives one period and makes a loan to one borrower and cannot diversify
its risk. If the borrower defaults, the bank su¤ers all the losses due to this default. As we
assume that banks hold no capital, these losses lead to immediate default of the bank itself.
4
See also applications to analyses of the labor market (Andolfatto, 1994; Merz, 1996 among others).
4
Probability of default Borrowers are represented by a single representative agent. To
borrow this agent pledges his assets as collateral. However, the bank can realise only a
fraction
of collateral. In case of a borrowers default, the bank will experience a loss only
b
t
if she lends in excess of : This loss is
Ht where Ht is the value of collateral in real
terms5 .
Each period, defaulted debt is a fraction of total debt
b
t
Ht
b
t Ht
=1
b
t
As the number of banks is in…nite, the law of large numbers holds and this fraction
re‡ects the probability of default of loans each bank faces.
It follows that the probability of success of a loan can be written as:
pt =
(1)
b
t
The bank has a non-pecuniary cost when she lends. This cost receives several foundations
by Diamond (1984) or by Haubrich (1989). It can either represent the disutility of bankruptcy
for bankers: the time they spend in the procedure, the time to explain poor results or the
consequences in terms of loss of reputation. In our framework, uncertainty about is another
potential microfoundation. For tractability, we assume that this cost has the following convex
form 2 Ht
2
b
t
. It is quadratic in the excess volume of debt Ht
value of the collateral.
b
t
, divided by the
is a parameter, which will be estimated.
Bank program Each bank i maximizes her pro…ts, by choosing a loan to value ratio
b;i
t .
As they are small, they are both price-takers and probability-takers. Banks do not internalize
the e¤ect of
b;i
t
neither on the loan rate rtl , the deposit rate rtd and the probability of success
pt , nor even on Ht . Finally the bank’s program is written:
max pt rtl
rtd
b;i
t
The …rst order condition gives
b;i
t
b;i
t Ht
2
Ht
b
t
2
as a function of the technological loan to value ratio
. Because of symmetry, we can drop the index i.
b
t
5
=
pt rtl
rtd
+
(2)
The distinction between real and nominal will become relevant when we assemble the …nancial ‡ows into
a New Keynesian DSGE framework.
5
We can understand
pt (rtl rtd )
> 0 as excess leverage over . Banks tolerate more leverage
and hence more risk as these risks are very likely to be fruitfull (pt ), when their interest
margin is large (rtl
rtd ), or when their internal cost to lend is low ( ).
Patient households Patient households own a claim on banks’assets. These claims are
then a mixture of debt contrats and claims on the pro…ts of banks. This correspondance
between assets owned by patient households and the liabilities of banks allows to …nd a
relationship between the di¤erent interest rates in the economy.
Patient households invest
earn 1 + rtl
b
t Ht
b
t Ht .
With probability pt banks are paid back by borrowers and
while with probability 1 pt banks get bankrupt and transfer
b
t
Ht
of losses to the depositors.
Depositors
rd bt Ht
pt
1
pt
Bankers
Total
rd ) bt Ht
(rl
Ht
0
rl bt Ht
Ht
Table 1: Flows of funds due to default
The returns for patient households is:
rtP = pt rtl + (1
pt )
1
b
t
Loan and deposit rates In our framework, there is a complementarity between risk
taking (
b
t
) and higher loan rates rtl .
rtl = rtd +
pt
(
b
t
)
As a consequence, the equilibrium should be such that the loan rate equals what the
borrowers would accept at most. However, a supply side determination of rtl such as the risk
of adverse sellection for too high values of rtl can also in‡uence the level of interest rates,
while menu costs can explain some a persistent inertia of loan rates (Neuwark and Sharpe,
1992). Altogether, given that the interest on deposits and/or the marginal re…nancing cost
of banks should be very sensitive to the interest rate set by the central bank rtm we just
assume that the following relation holds :
6
rtl
rtd = f (rtm )
(3)
with f () a decreasing function of rtm . Such property is also obtained if there is a risk
taking channel of monetary policy6 .
2.1.2
Bank market equilibrium
De…nition 1 Given
b
t
of
t
, rtm and Ht , a banking market equilibrium is determined by a value
which veri…es the program of banks (2), the choice of default (1) and the interest rate
equations (3). As banks are identical, I can consider only symmetrical equilibria.
Proposition 1 Using (1), (2), and (3), I …nd that a necessary condition for an equilibrium
is to verify
b
t
solution of
b
t
b
t
1
=
f (rtm )
The only feasible solution is:
b
t
1+
=
q
1+4
f (rtm )
2
This solution is not optimal as the central planner solution is:
b
t
=
Proof. See appendix.
The equilibrium lending ratio is above the ratio that the repossession technology would
imply. The di¤erence between the two is the result of three factors:
1. an interest rate margin factor which is likely to depend on the monetary policy interest
rate;
2. the impact of the non pecuniary cost ( );
3. repossession technology itself ( ). The better it is, the smaller the scale of risk shifting.
Hence excess leverage should decreases as loans enforcement improves.
6
See Dubecq, Mojon and Ragot (2009) for a model with risk shifting where the level of interest rates
in‡uences the risk perception of lenders. Lower interest rates increase the underestimation of risks and the
supply of funds to …nancial intermediaries. See Altumbas, Gambacorta and Marques (2010) and papers by
Jimenez, Ongena and Peydro (2010) for empirical evidence on this channel.
7
As the central planner solution is below the bank equilibrium, there is a role for policy
intervention on the loan-to-value ratio in the business cycle. We understand these interventions as macroprudential policies, which tax the volume of debt lent by banks.
2.1.3
Welfare and policy
The welfare implications of risk-shifting externalities are not straightforward. The presence
of di¤erent agents, some of which are liquidity constrained, may imply that the form of
overborrowing considered in this paper has welfare-improving e¤ects. We assume here that
the social planner cannot enforce transfers that would be Pareto improving. This assumption
gives rise to a constrained Pareto optimum along the lines of Lorenzoni (2008) or more
recently Korinek (2010) or Stein (2010).
In our model, an obvious Pareto improving policy would consist of taxing …nancial intermediairies’revenues when their loans are successful, in order to limit their incentive to take
risk. However a tax on banks’pro…ts may not bet the best tool because its e¤ects should be
contingent on the business cycle. In the real world, tax policies are usually modi…ed at most
once a year. As a result, there is room for other tools than direct taxes on pro…ts. Credit
limits or "cap and trade" schemes as discussed in Stein (2010) may be an option.
7
In this paper, we restrict the analysis to the comparative e¢ ciency of various combination of state contingent rules, such as the Taylor rule. The policy makers can choose the
coe¢ cients of such rules in order to minimise an ad hoc loss function of which the arguments
are the variance of in‡ation, the output gap and the interest rate. This approach falls short
of a genuine welfare analysis. It has been widely used in the literature on optimal monetary
policy.
2.2
A General equilibrium with risk shifting
The main contribution of this paper is to introduce the mechanism described in the previous
section into a general equilibrium framework. We chose the latter to be the Iacoviello
(2005) model which is both well known and relevant to decribe the housing debt cycles
that led to the great recession. In Iacoviello (2005), the economy is populated by di¤erent
agents: patient households, impatient households and entrepreneurs. In equilibrium, patient
households will lend to entrepreneurs and impatient households up to a collateral value
7
See also the welfare analysis in Gertler, Kiyotaki and Queralto (2010).
8
of their housing assets. Households consume, work, lend or borrow and demand houses.
Entrepreneurs consume, produce an homogenous good using labor, real estate and capital. In
order to get nominal rigidities, the homogenous good is sold to retailers who face monopolistic
competition and price stickiness à la Calvo. A central bank controls the nominal interest
rate following a Taylor-type rule.
In addition to that framework, there are intermediairies as described in the previous
section. These intermediairies gather deposits from patient households and lend them to
both entrepreneurs and impatient households. Moreover, these intermediairies are owned by
patient households.
Finally, debt are just one period contracts to preserve the tractability of risk sharing
IM
t
within families. In this section, we will denote
and
e
t
the probabilities of default for
impatients households and entrepreneurs. They correspond to 1 pt of the partial equilibrium
model.
2.2.1
Patient households
In each period t patient housholds consume cPt , own hPt units of housing and provide ltP units
of labour. They maximize a lifetime utility function:
E0
1
X
P t
ln cPt + jP ln hPt
P
where
!
is their discount factor. Their budget constraint is the following:
cPt
BtP 1
P
P
t=0
where
ltP
+ qt
hPt
+
Rt BtP
1
t
= BtP + wtP ltP + Ft
is the amout of assets held between t
the nominal gross interest rate and
t
1 and t Ft is the pro…ts of retailers, Rt is
is the gross in‡ation rate between t
1 and t. wtP is
the real wage per hour for patient households. Rt is stochastic because of the risk of default.
The …rst order conditons are standard:
1
cPt
=
wtP
=
qt
cPt
=
P
Rt+1
P
t+1 ct+1
Et
ltP
P
1
cPt
jP
+
hPt
9
P
Et
qt+1
cPt+1
2.2.2
Impatient households
Impatient households di¤er only slightly from patient households: their discount factor is
higher (
IM
P
<
). As a consequence, they borrow from patient households through inter-
mediairies. However, the amount they can borrow is constrained. Each period t impatient
IM
households consume cIM
and provide ltIM unit of labour. They maximize a lifetime
t , own ht
utility function:
E0
1
X
IM t
ln cIM
t
+
ltIM
jIM ln hIM
t
IM
t=0
where
IM
IM
!
(4)
is their discount factor and their budget constraint is the following:
RtIM1 BtIM1
cIM
+ qt hIM
+
t
t
= BtIM + wtIM ltIM +
t
IM
t
The deviation from Iacovello (2005) is the introduction of the term
(5)
IM
t ,
which is the
gain from default, as detailled thereafter.
The borrowing constraint implies that the amount of debt BtIM is limited by a fraction
of the present value of the collateral:
hIM
IM
t qt+1
E
t
t
Rt
BtIM
t+1
The discount rate used in the present value is the rate faced by patient households as
they are those who seized the collateral when default occurs.
IM
t
is the bank based loan to
value ratio.
First order conditions are:
1
cIM
t
=
IM
Et
IM
t
IM
c
t+1 t+1
RtIM 1
+
t Rt
1
lIM IM
= t IM
ct
0
qt+1
1+
cIM
IM
t+1
@
+
Et
+ IM
t+1
wtIM
qt
cIM
t
1+
hIM
t
h
hIM
t 1
=
jIM
hIM
t
hIM
t+1
+ t IM
h hIM
t qt+1
t
IM qt+1 RtIM
t qt+1
t
Rt
t+1
1
A
These …rst order conditions di¤er only slighlty from Iacovello (2005) through the impact of
the probability of default
IM
t .
This probability a¤ects impatients households both through
the Euler equation and the equation for house accumulation. In the former it reduces the
10
expected interest rate paid on debt. In the latter, it intervenes through the e¤ects of excess
debt on the stock of houses.
Default Finally the probability of default follows the rule introduced in the partial equilibrium section. In the dynamic version of the model, we introduce some persistence through
a dependance on its lagged value.
In the partial equilibrium model presented in the previous section, the maturity of debt is
only one period and the default occurs on all the excess debt that was issued in the previous
period. However the compared dynamic of the default rate and the debt is obviously more
complex (see …gure ??). The rise in defaults takes place several quarters after the amout
of debt has begun to increase. If one understands this increase in debt above the trend as
excess debt in the sense de…ned previously8 , data point to a delayed e¤ect of this increase
on delinquencies.
Our understanding of this lagged e¤ect is that there are some rigidities in the spreading
of news such as costs to acquire information. Under the condition that these rigidities leave
unchanged the sign of the response at impact, there still exits an equilibrium.
Whatever the rigidities on the probabilities, we can show that under loose conditions, an
equilibrium exists as long as :
IM
t
The coe¢ cient
IM
=
IM
IM
t
+ 1
t
IM
t
1
IM
(6)
captures the inertia of the default rate. Then we can precise the gains
for impatient households to default:
IM
t
2.2.3
=
IM
t
qt
IM
t Et 1
Rt
IM
t Rt 1
1
t
t qt
LIM
t 1
(7)
Entrepreneurs
Entrepreneurs consume and produce. They hire labor from both patient and impatient
households, that they combine with physical capital and real estate to produce goods. They
also maximize a lifetime utility function, with a lower discount factor
E0
1
X
( e )t ln cet
t=0
The production function is Cobb-Douglas:
8
See also the empirical results of Alessi and Detken (2009).
11
e
het
Yt = A (Kt 1 )
(1
ltIM
1
)
(1
ltP
)(1
)
And budget constraint is the following:
Yt
+ Bte +
Xt
cet + qt het +
Rte 1 Bte
1
+ wtP ltP + wtIM ltIM + It +
t
e;t
e
t
+
=
K;t
The capital acculumation follows:
Kt = It + (1
) Kt
1
They also face a borrowing constraint:
het qt+1 t+1
e
t Et
Rt
Bte
First order conditions are:
1
=
cet
wtIM = (1
qt
cet
1+
het
h e
ht 1
)
jP
+
het
=
e
Et
e
t+1 ct+1
Yt
IM
Nt Xt
e
e
t+1
Rte 1
0
Et @
+
wtP = (1
qt+1
cet+1
1+
t Rt
)(1
)
het+1
+
h het
e e
R
e
t t 1
t
Rt 1
Yt
NtP Xt
IM
t t qt+1
qt
t+1 +
1
A
and the standard equations for the shadow value of capital. We …nd here similar di¤erences with Iacovello (2005) as for impatient households: default interfers with the expected
interest rate in the Euler equation and on the housing accumulation equation. Similarly
default gives the following equations:
e
t
The coe¢ cient
e
=
e
e
t
+ 1
e
t
e
t
1
captures the inertia of the default rate. Then we can precise the gains
for entrepreneurs to default:
e
t
=
e
t
qt
e
t Et 1
e
t Rt 1
Rt
12
1
t
t qt
Let
1
2.2.4
Retailers
Sticky prices are introduced through a retail sector. A continuum of …rms buys the homogenous good produced by entrepreneur to a wholesale price PtW . Each …rm j transforms
this good into a di¤erentiated good indexed by j. Then they face monopolisitic competition with price stickiness à la Calvo, i.e. they can reset their price with a probability
p.
Following Christiano, Eichenbaum and Evans (2005), we further assume that prices are set
before shocks are observed and that, when prices are not reset, they evolve as past in‡ation. This gets us the standard new keynesian Philips curve with full indexation and price
predetermination, which can be written, in its log-linearized form as:
^t =
where
2.2.5
=
(1
p )(1
(1+ )
p
p
)
1
1+
P
and
t
^t
1
+
P
1+
Et 1 ^ t+1 + Et 1 Xt
P
= log (Pt =Pt 1 ) and Xt =
PtW
Pt
.
Intermediairies
Intermediaries are modelled along the lines of the previous section adapted to the current
framework. Now, there is two continuum of intermediairies. Each small bank is specialized
either in lending to households or in lending to entrepreneurs. Both kinds of banks are owned
by patient households, who are depositors at the same time. Due to market completness
within families (see Lucas (1990)), patient households are fully insured against the risk of
owning or to be depositors in one bank.
As a consequence, the return of lending is the average of the returns of lending to impatient households and to entrepreneurs weighted by the volume of debt each kind of agent
has borrowed:
Rt
1 =
t
BtIM
BtIM + Bte
pIM
RtIM
t
Bte
BtIM + Bte
pet
1 + 1
pIM
t
IM
t
1
+
t
(Rte
1) + (1
pet )
e
t
1
which is consistent with a linear relation between the intermediation spread and the interest
rate set by the central bank:
Rtl
Rtd =
13
+ Rtm
2.2.6
The central bank
In the benchmark version of the model, which is the one we estimate, the central bank
follows a Taylor-type reaction function. The current nominal interest rate is set with respect
to past in‡ation and past deviations from steady state output Y . There is some inertia in
this reaction measured by the coe¢ cient
m.
We allow shocks to this reaction function eR;t ,
which follow a white noise.
Rtm = Rtm 1
2.2.7
Yt
Y
r
t 1
m
1
1
ry
m
rr
eR;t
Equilibrium
Given an exogenous process of shocks, in this economy, an equilibrium is a sequence
IM
e e IM
m
e
IM
e
IM
t ; t ; pt ; pt ; rt ; Rt ; Rt ; Bt ; Bt ; :::
(8)
which solves the problems of the entrepreneurs, the patient housholds, the impatient households, the retailers and the intermediairies, the clearing market condition and the central
bank’s reaction function.
2.3
Monetary and macroprudential policies
We consider three policy regimes
1. The Plain Vanilla Taylor rule (PVTR regime). This is the benchmark case where
the monetary policy instrument, i.e. the short-term interest rate, follows a standard
Taylor rule. It increases with in‡ation and the output gap.
Rtm = Rtm 1
m
r
t 1
Yt
Y
1
1
ry
m
rr
eR;t
2. The Augmented Taylor rule (ATR regime): this is regime where monetary policy leans against …nancial winds, i.e. we augment the Taylor rule with an argument
whereby the short term interest rate increases with the credit growth.The augmented
Taylor rule can be expressed as:
Rtm
=
Rtm 1
m
r
t 1
Yt
Y
14
1
ry
debtt
debtt 1
1
rd
rr
m
eR;t
3. Independent Macroprudential Policy (IMP regime): Authorities can a¤ect both
the short-term interest rate similarly to the previous case and, independently from
monetary policy decisions, the cost of credit. Monetary policy is modelled as in the
PVTR regime and the Macroprudential policy take the following form, where the higher
is the more agressive in the leaning against …nancial winds.
e
t
IM
t
3
=
=
e1 +
IM
q
1+
1+4
q
+ dev(rtm )
e
2
1+4
debtt
debtt 1
+ dev(rtm )
IM
2
debtt
debtt 1
Estimation and simulation results
We estimate this model on the euro area, the UK and the US using six macroeconomic
series: real GDP, real consumption, in‡ation, the 3-month Euribor.and also two series for
house prices and for household credit. This completes the estimates of Iacoviello and Neri
(2009) which, to our knowledge, are available only for the US.9
As these series10 are not available for the whole euro area, we approximate them by
aggregating the corresponding series for the major member countries. The sample covers
the period 1985:1 2009:4. Series are detrended separately.
We deliberately measure …nancial developments with a loan volume series rather than
with an interest rate. The observable interest rate on loan transactions is unfortunately
a very incomplete measure of credit conditions, while, arguably, loan volume can better
capture how loan demand accomodates changes in lending supply conditions.
Following Smets and Wouters (2007), we estimate …rst the mode of the posterior distribution. This estimation is undertaken by usual likelyhood maximization. We then implements
(in Dynare) the Metropolis-Hastings algorithm to obtain the complete posterior distribution. The estimation focuses on the dynamic parameters. Those parameters are divided into
two groups: shocks (variances and persistences) and dynamic parameters inside the model
(Taylor rule, habit formation, Calvo lottery, adjustment costs).
9
E¤ectively, the estimation of the model’s parameter spe…ci…ed either with or without risk shifting would
be similar. In the version without risk shifting, which is similar to Iacoviello (2005) credit supply shocks
take the form of ad hoc changes in the loan to value ratio imposed on the borrowers.
10
TBC with a list of variables rebuild this way + a brief description of the methodology.
15
We set the prior distribution of the model parameters along the line of the literature. A
set of parameters of the model is calibrated following Iacovello (2005). Table 3 summarizes
these values.
D e sc rip tio n
Va lu e
P referen ces
P
IM
e
d isc . fa c to r p a tie nt
.9 9
d isc . fa c to r im p a tie nt
.9 5
d isc . fa c to r e ntre p .
.9 8
P roduction fun ction
re p la c . ra te o f c a p .
.0 2 5
sh a re o f c a p ita l in p ro d .
.3
sh a re o f re a l e sta te
.0 3
sh a re o f p a tie nt in la b o r
.6 4
Risk-shifting
e
ste a d y -sta te p ro b . o f d e fa u lt o f e ntre p .
0 .0 1 5
IM
ste a d y -sta te p ro b . o f d e fa u lt o f im p a t.
0 .0 2
Table 2: Parameters
Detail reports on priors and posteriors are available upon request from the authors.
TBC
3.1
Dynamics of the economy for various policy regimes
In view of the potential con‡icts between the two objectives of price and …nancial stability
in some circumstances, i.e. when the economy is mainly driven by supply shocks, the next
important question is to assess whether these con‡icts can be material in the business cycle.
The assessment of the relative importance of various supply and demand shocks is is the
object of an (hopefully) endless academic literature that goes much further the scope of this
paper.11
A …rst pass on this question is to report how important such shocks have been according
to the model estimation. The variance decomposition of in‡ation, output, the short-term interest rate, credit and housing prices are reported in TABLE 2.1. These estimates correspond
to the last 25 years for the UK, the US and the euro area.
Several results are worth underlining. First, investment speci…c shocks dominate the
variance of ‡uctuations in investment, consumption and GDP while mark up shocks dominate
the variance of in‡ation in the three areas. Second, housing preference shocks dominate the
11
See for examples of the debate on the US business cycle Gali (1999), Justiniano and Primiceri (2008),
Smets and Wouters (2007), Chari, Kehoe and McGrattan (2007), Fisher (JPE-200?) and references therein.
16
variance of housing prices and of credit, while credit supply shocks impact only the credit
developments. This is a limit of the model which may fail to capture the quasi trend evolution
of credit and its impact on demand.
Third, there also some sharp di¤erences across the areas. Productivity shocks are estimated to have a much larger e¤ects on GDP and its components in the US than in the euro
area and the UK.
These variance decompositions point to the shocks that are the most important ones for
the variance of in‡ation: mark up shocks for the three countries and to a lesser extent housing
preference shocks, private demand shocks in the euro area, investment speci…c shocks in the
UK and, in the case of the US, productivity shocks.
In any events, Figure 2.1a, b and c report the response of in‡ation to the “structural
shocks” of the model for the euro area, the UK and the US, comparing it across the three
policy regimes. With the exception of two shocks, the response of in‡ation is almost identical
across regimes.
This similarity across regime is the most striking for the cost push shocks but it also
evident for shocks to productivity, investment speci…c technology, residual demand, and
monetary policy.
These …ve shocks account altogether for nearly 80% of the variance of euro area in‡ation,
and over 80 % in the US and the UK. This similarity in the response of in‡ation across regimes
is consistent with the results of Angelini, Neri and Panetta (2010). [OTHER REFERENCES
to be added ]
It therefore seems that the introduction of an independent macroprudential policy would
not modify the dynamics of in‡ation for several of the usual determinants of in‡ation.
There are some shocks, however, of which the e¤ects on in‡ation di¤er across regimes.
The most striking case is the housing preference shock (Figure 2.7). In‡ation initially declines
on impact but its response is much larger and more persistent in the case of the augmented
Taylor rule regime. This is because the increase in the real interest rate triggered by the
increase in credit weighs on the output gap and in‡ation. On the contrary, the output gap is
positive in the benchmark case of a simple Taylor rule. In‡ation remains closest to baseline
in the regime 3. This is because the increase in house prices does not translate into a credit
boom that stimulates demand and in‡ation.
The other interesting case is the one of the credit supply shock. There again, we observe
a di¤erent response of in‡ation across monetary policy regimes. By construction, the shock
17
to credit (negative in Figure 2.8) is muted in the AMP regime. And so is the output gap
and in‡ation.
Altogether, this qualitative exploration of the circumstances that can lead to a con‡ict
between monetary policy and macroprudential policies delivers two very clear conclusions:
For a number of shocks, which are typically the dominant drivers of in‡ation, the policy
regime is irrelevant for the dynamics of in‡ation. For shocks to asset prices and credit
supply, the combination of an independent macro-prudential lean against credit policy and
a monetary policy focused on in‡ation is the best for price stability. This is because such
a macro-prudential policy can shield the business cycle from the perturbations initiated in
the …nancial sector.
These simulation based results re‡ect a number of modeling hypotheses and uncertainty
of various kinds. Other simulations (e. g. Kannan et al, 2009; with a fully calibrated model
which structure is very similar to the one used here) point to the possibility of con‡icts
folowing productivity shocks. In‡ation declines, production increases and and so do credit
and house prices. Asset prices, and credit, front load the expected increase in wealth associated to faster productivity growth. Hence, leaning against credit, either with the short-term
interest rate or by raising the cost of credit takes in‡ation further away from its baseline than
in the Plain Vanilla monetary policy regime case. However, this mechanism is not estimated
to imply large di¤erences in the response of in‡ation across policy regimes (see the bottom
left panel of Figures 2.1a b and c).
3.2
The e¢ cient combination of monetary and MP policy
Now, we can also use the model to optimize the coe¢ cients of the contingent rules of each
policy regime. We focus our attention on the variance of in‡ation, output and the shortterm interest rate, following the common practice in the literature on monetary policy. We
assume that policy makers aim at minimizing a loss function that admits as its arguments
‡uctuations in in‡ation, output, and the interest rate. For the monetary policy rule based
on optimized coe¢ cients, the ‡uctuations of credit are also considered (the weight for credit
equals 1, as for the other components).
Loss =
2
+
2
y
+
2
R
Given that this exercise is purely illustrative, the optimization assumes a weight of 1 on
each of the three inputs. We also consider that any level of the short-term interest rate can
18
be atteined, thereby ignoring the possibility that the Zero Lower Bound may prevent the
central bank to be on this "virtual" optimal path.
The optimization is run separately for each of the three policy regimes. Table 2.2 reports
the optimal values of the policy rule parameters for the euro area, the UK and the US. These
should be compared to the ad hoc coe¢ cients of 1.5 for in‡ation, 0.5 for the output gap, 0.5
for the credit growth term in the augmented Taylor rule and 0.5 for
in the “lean against
credit”instrument.
Some results of the optimization are strikingly similar across countries. First, the strength
of the interest rate reaction to credit growth is much smaller than the one we had assume
in the simulations reported above. This is because our main interest being the potential
nuisance of leaning against credit for the traditional objectives of monetary policy our loss
function does not imbed the objective of credit stabilization per see. Hence, variations in
interest rates for credit stabilizations purposes brings little for in‡ation and output stabilization while it increases the loss function through higher volatility of the short term interest
rate.
Second, there are some bene…ts for in‡ation and output stabilization in raising
closer
to 1. As can be seen from …gures 2.1 to 2.9, a coe¢ cient of 0.5 already implies a sharp
deviation of credit from its trajectory in Regime 1 and 2 that do not have the ability of
directly leaning against credit.
Other results di¤er across countries. Somewhat surprisingly the smoothing coe¢ cient of
the interest rate is much larger in the augmented Taylor rule regime than in the other two
regimes in the euro area and in the UK.
The e¤ects of the optimization can be judged from the gain in the variance of in‡ation,
output, the interest rate and credit as reported in Table 2.3.
3.3
Would have Macroprudential policy prevented the Great Recession?
Our next step is to simulate the trajectories of in‡ation, GDP, credit and the interest rate
between 2000 and 2010 for each of policy regime, using the optimized reaction functions
presented in Table 2.3. We present these simulations for the euro area.
Results are reported in Figure 3.1, for a simulation that ignores the ZLB on the shortterm interest rate. Not surprisingly, the e¢ cient rules suggest very negative interest rates at
19
the time of the Great Recession in 2008 and 2009.
In Figure 3.2 we report a simulation where we add "positive monetary policy shocks" for
the quarters where the e¢ cient rules would have suggested a negative value for the nominal
short-term interest rate. These calculations are tedious and we could not yet have an interest
rate exactly at the Zero lower bound. The comparison of the two simulations are still usefull
to quantify the impact of the ZLB and its severity across the policy regimes.
The results are very impressive in the sense, if it were not for the ZLB, either the e¢ cient
Plain Taylor rule or the e¢ cient combination of the Plain Taylor rule and the autonomous
"lean against credit macroprudential policy" would have very much limited the depth of the
recession in the euro area. Two important caveat are in order, however. First, following these
optimal rules would have meant keeping the short-term interest rate in negative territory
for several quarters in 2008 and 2009 and taking the nominal interest rate to very negative
levels. Second, the realized in‡ation is much lower to the ECB de…nition of price stability
than in the track reccord of the ECB. This re‡ects in part that the loss function gives an
equal weight to deviations of in‡ation, output and the interest rate. The ECB has in fact
very e¤ectively focused on its primary objective of price stability.
We also notice that the need for "negative interest rates" is much reduced if public
autorities could recourse to an independent macroprudential policy. As evident from the
comparison of Figure 3.1 and 3.2, the ZLB implies a deeper recession if the short-term
interest rate is the only policy intrument available to the authorities.
References
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110(460), pp. 236-55.
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[4] Angeloni, I. and Faia, E. (2009), “A Tale of Two Policies: Prudential Regulation and
Monetary Policy with Fragile Banks”, Kiel Working Papers 1569, Kiel Institute for the
World Economy.
20
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[6] Caballero, R. and Krishnamurthy, A. (2003), “Excessive Dollar Debt: Financial Development and Underinsurance”, Journal of Finance, vol. 58(2), pp. 867-894.
[7] Chari, V., Kehoe, P. and McGrattan, E. (2007), “Business Cycle Accounting”, Econometrica, , vol. 75(3), pp. 781-836.
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[10] Dubecq, S., Mojon, B. and Ragot, X. (2009), “Fuzzy Capital Requirements, RiskShifting and the Risk Taking Channel of Monetary Policy”, Documents de Travail 254,
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[12] Gerali, A., Neri, S., Sessa, L. and Signoretti, F. (forthcoming), “Credit and banking in
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21
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22
Appendix
Proof of proposition 1.
b l
t ; rt
(1), (2), and (3) yield the following system for
b
t
pt rtl
=
rtd =
rtl
rtd ; pt :
rtd
+
t
+ rtm
t
pt =
Divided by
equation for
t
b
t
which is di¤erent of 0, these three equations are summarized by a single
b
t
b
t
t
b
t
t
1
rtd
rtl
=
t
It is a second degree polynom, which as two solutions which are:
b
t
b
t
As
b
t
=
=
t
t
1+
q
1+4
q 2
1+4
1
+ rtm
t
t
+ rtm
t
0
2
has to be positive, the only feasible solution is the …rst one.
The central planner internalizes the e¤ect of excess debt on the probability of success.
As a consequence, his problem for the bank turns to be:
max rtl
b
t
t
rtd
Ht
2
b
t
Ht
t 2
and the solution is:
b
t
=
t
Loan rate and monetary policy
In this appendix, we show why the complementarity faced by intermediairies between higher
loan rates and more excess lending is ruled out by patient households. Indeed patients
households expect a net rate of return rtP on their savings such that:
2
rtP
=
rl
b t
t
1
23
b
t
However rtP is set by their Euler equation and is independant of the terms on the right hand
side. Thus we can di¤erentiate this expression with respect to
b
t,
given that the derivative
of rtP equals 0:
1
b
t
rtl
1+
=
b
t
@rtl
@ bt
At the equilibrium loan rate, we have:
rtl
, (1
rtl )1 +
s
1+
1+4
rtl
=0
b
t
rtm
=2
The last expression de…nes a function f such that f (rtl ) = 2. This function is stricly
decreasing for small value of rtl which means that there exits only one solution to the equation
f (rtl ) = 2. We check that this solution is between
numerically.
24
1
r
1 and
1
e
1 as f (
1
e
1) = 1:99 < 2
Table 2.1: Variance decomposition in of macroeconomic variables between 1985 & 2010
Euro area
Invest.
Housing Credit Mon Demand
Productivity
Demand
Specif.
pref
sup.
Pol
gx
GDP
48.01
1.25
4.05
13.88
0.26
8.10
3.88
Consumption
36.76
1.47
4.38
20.66
0.26
9.58
1.69
Investment
83.47
0.47
2.47
3.96
0.25
2.85
0.40
Interest rate
1.73
31.25
46.38
3.97
0.69
5.68
5.08
House prices
2.26
1.57
0.13
94.69
0.09
0.25
0.30
Credit
0.18
0.02
0.18
43.56
55.85 0.08
0.02
Wages
3.96
56.30
1.37
25.56
0.15
4.94
6.28
Inflation a
4.69
9.57
18.11
18.46
0.24
6.99
1.92
Mark
up
20.56
25.20
6.13
5.23
0.70
0.12
1.44
40.02
United Kingdom
GDP
Consumption
Investment
Interest rate
House prices
Credit
Wages
Inflation a
Invest.
Specif.
67.36
62.33
89.75
5.65
10.59
0.19
15.97
14.58
Productivity
Demand
0.65
1.20
0.03
17.13
5.23
0.00
40.53
10.25
0.87
1.15
0.51
42.96
0.14
0.04
0.62
5.93
Productivity
Demand
48.95
60.39
18.78
17.30
11.45
70.24
4.82
67.48
0.09
0.08
0.10
3.65
42.41
0.13
0.28
0.31
Housing
pref
2.99
3.50
3.79
12.08
80.77
50.62
22.68
4.08
Credit
sup.
0.32
0.36
0.26
2.19
0.30
49.03
0.37
0.50
Mon
Pol
3.20
4.53
0.88
5.72
0.30
0.02
4.01
5.30
Demand
gx
9.77
5.24
1.26
3.35
1.01
0.03
12.96
4.71
Mark
up
14.85
21.69
3.53
10.91
1.67
0.07
2.86
54.65
Housing
pref
0.08
0.10
0.13
7.86
5.46
6.21
33.40
9.17
Credit
sup.
0.01
0.01
0.02
0.20
2.80
0.01
56.49
0.16
Mon
Pol
0.07
0.08
0.04
2.04
4.13
0.03
0.03
2.68
Demand
gx
6.66
2.80
2.17
3.98
6.58
0.57
0.47
6.30
Mark
up
4.76
5.78
2.09
60.43
20.35
1.79
0.86
8.72
United States
GDP
Consumption
Investment
Inflation
Interest rate
House prices
Credit
Wages
Inflation a
Invest.
Specif.
39.37
30.75
76.68
4.53
6.81
21.01
3.64
5.17
Table 2.2: Coefficients of the policy rules
Smoothing
Inflation
GDP
0,82
0,59
0,81
1,72
2,10
2,02
0,43
0,73
1,00
Estimated talor rule coefficients
Euro area
UK
US
Ad hoc policy coefficients (used to computed the IRFs of Figures 2.1 to 2.9)
Plain Taylor rule
0,75
1,50
0,50
Augmented Taylor rule
0,75
1,50
0,50
PTR + macroprudential
0,75
1,50
0,50
Credit
Macro Prudential Policy
0,50
0,50
Optimized coefficients for the euro area
Plain Taylor rule
0,32
Augmented Taylor rule
0,67
PTR + macroprudential
0,31
1,63
1,51
1,63
1,33
0,21
1,35
0,04
-
0,74
Optimized coefficients for the UK
Plain Taylor rule
0,327
Augmented Taylor rule
0,842
PTR + macroprudential
0,326
1,58
1,50
1,58
1,05
0,16
1,05
0,14
-
0,53
Optimized coefficients for the US
Plain Taylor rule
Augmented Taylor rule
PTR + macroprudential
1,45
1,49
1,45
1,49
0,55
1,49
0,11
-
0,73
0,59
0,67
0,59
Table 2.3: Stabilisation effects of optimized policies
PIB
CPI
Euro area
Data 1985-2010
0,62
0,41
Plain Taylor rule
0,20
0,49
Augmented Taylor rule
0,65
0,28
PTR + macroprudential
0,17
0,39
TX
Loss
Credit
0,74
0,96
0,61
0,82
2,69
2,39
2,27
1,82
0,93
0,75
0,73
0,44
UK
Data 1985-2010
Plain Taylor rule
Augmented Taylor rule
PTR + macroprudential
0,81
1,28
1,38
0,93
0,80
2,17
0,89
1,28
4,08
7,25
7,80
4,43
1,81
3,08
3,05
1,76
US
Data 1985-2010
0,81
0,57
Plain Taylor rule
0,44
0,65
Augmented Taylor rule
0,77
0,36
PTR + macroprudential
0,39
0,46
note: loss is the sum of the first three columns
0,86
0,64
0,57
0,74
3,31
2,84
2,81
2,28
1,07
1,12
1,10
0,68
0,66
0,73
2,48
0,45
Figure 2.1a:Effects of Various Shocks on Inflation, Euro Area
-4
2
Residual Demand
x 10
2
1
0
0
-2
-1
-4
-2
0
10
-4
10
20
30
40
-4
-6
0
Time Preferences
x 10
x 10
0
x 10
Investment
10
-4
20
30
40
30
40
30
40
Monetary Policy
-2
5
-4
0
-5
-6
0
10
-4
1
20
30
40
-8
0
Financial
x 10
5
0.5
0
0
-5
-0.5
-10
-1
0
10
-4
5
20
30
40
-15
0
Productivity
x 10
2
0
x 10
10
-4
x 10
10
-3
20
Cost Push
0
-5
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-2
-10
-15
20
Housing Preferences
0
10
20
30
40
-4
0
10
20
30
40
Figure 2.1b:Effects of Various Shocks on Inflation, UK
-4
5
Residual Demand
x 10
1
0
0
-5
-1
-10
-2
-15
0
10
-4
15
20
30
40
0
10
-3
-3
0
Time Preferences
x 10
x 10
x 10
Investment
10
-3
20
30
40
30
40
30
40
Monetary Policy
-0.5
5
-1
0
-5
0
10
-4
2
20
30
40
-1.5
0
Financial
x 10
1
x 10
10
-3
20
Housing Preferences
0
0
-1
-2
-4
-2
0
10
-3
1
20
30
40
-3
0
Productivity
x 10
5
0
20
Cost Push
0
-1
-5
-2
-10
-3
x 10
10
-3
0
10
20
30
40
-15
0
Taylor Rule
Macro Prudential
Augmented Taylor Rule
10
20
30
40
Figure 2.1c:Effects of Various Shocks on Inflation, US
-4
5
Residual Demand
x 10
5
0
0
-5
-5
-10
-10
-15
0
10
-4
15
20
30
40
0
10
-4
-15
0
Time Preferences
x 10
x 10
x 10
Investment
10
-3
20
30
40
30
40
30
40
Monetary Policy
-0.5
5
-1
0
-5
0
10
-4
4
20
30
40
-1.5
0
Financial
x 10
1
x 10
10
-3
20
Housing Preferences
0
2
-1
0
-2
-2
0
10
-3
0
20
30
40
-3
0
Productivity
x 10
5
-1
x 10
10
-3
0
-2
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-5
-3
-4
20
Cost Push
0
10
20
30
40
-10
0
10
20
30
40
Figure 2.2:Cost Push Shock, Euro Area
-3
1
Credit
x 10
x 10
1
0
0
-1
-1
-2
-2
-3
-3
-4
0
10
-3
3
20
30
40
-3
-4
0
Inflation
10
Investment
x 10
20
30
40
30
40
GDP
0.03
2.5
0.02
2
1.5
0.01
1
0.5
0
10
-4
4
20
30
40
0
0
Policy Rate
x 10
x 10
8
2
6
0
4
-2
2
-4
0
10
20
30
40
0
10
-5
20
Real House Prices
Taylor Rule
Macro Prudential
Augmented Taylor Rule
0
10
20
30
40
30
40
30
40
Figure 2.3:Investment Specific Shock, Euro Area
-3
2
Credit
x 10
x 10
2
1
-4
Inflation
0
0
-2
-1
-4
-2
-3
0
10
-3
10
20
30
40
-6
0
10
Investment
x 10
20
GDP
0.02
0.015
8
0.01
6
0.005
4
0
10
-4
2
20
30
40
0
0
Policy Rate
x 10
4
x 10
10
-5
20
Real House Prices
2
1
0
-2
0
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-4
-1
0
10
20
30
40
-6
0
10
20
30
40
Figure 2.4:Monetary Policy Shock, Euro Area
-3
5
Credit
x 10
0
4
x 10
-4
Inflation
-2
3
-4
2
-6
1
0
0
10
-3
-0.5
20
30
40
-8
0
10
Investment
x 10
20
30
40
30
40
GDP
0
-1
-0.005
-1.5
-0.01
-2
-0.015
-2.5
-3
0
10
-4
10
20
30
40
-0.02
0
Policy Rate
x 10
0
x 10
10
-5
20
Real House Prices
-2
5
-4
0
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-6
-5
0
10
20
30
40
-8
0
10
20
30
40
30
40
30
40
Figure 2.5:Productivity Shock, Euro Area
-3
4
Credit
x 10
5
2
0
0
-5
-2
-10
-4
0
10
-3
3
20
30
40
x 10
-4
-15
0
Inflation
10
Investment
x 10
20
GDP
0.015
0.01
2
0.005
1
0
0
-1
-0.005
0
10
-3
1
20
30
40
-0.01
0
Policy Rate
x 10
15
0
10
-1
5
-2
0
-3
0
10
20
30
40
x 10
-5
0
10
-5
20
Real House Prices
Taylor Rule
Macro Prudential
Augmented Taylor Rule
10
20
30
40
Figure 2.6:Residual Demand Shock, Euro Area
-3
3
Credit
x 10
x 10
2
2
1
1
0
0
-1
-1
0
10
-3
1
20
30
40
-4
-2
0
Inflation
10
Investment
x 10
20
30
40
30
40
GDP
0.015
0.5
0.01
0
-0.5
0.005
-1
-1.5
0
10
-4
6
20
30
40
0
0
Policy Rate
x 10
2
x 10
10
-5
20
Real House Prices
0
4
-2
2
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-4
0
0
10
20
30
40
-6
0
10
20
30
40
30
40
30
40
Figure 2.7:Housing Preferences Shock, Euro Area
Credit
0.05
5
0.04
x 10
-4
Inflation
0
0.03
-5
0.02
-10
0.01
0
0
10
-3
6
20
30
40
-15
0
10
Investment
x 10
20
GDP
0.01
4
0
2
-0.01
0
-2
0
10
-4
5
20
30
40
-0.02
0
Policy Rate
x 10
3
0
2.5
-5
2
-10
0
10
20
30
40
x 10
1.5
0
10
-4
20
Real House Prices
Taylor Rule
Macro Prudential
Augmented Taylor Rule
10
20
30
40
Figure 2.8:Financial Shock, Euro Area
Credit
0
1
-0.02
0.5
-0.04
0
-0.06
-0.5
-0.08
0
10
-4
5
20
30
40
-4
-1
0
Investment
x 10
x 10
1
x 10
Inflation
10
-3
20
30
40
30
40
30
40
30
40
30
40
GDP
0
0
-1
-5
-2
-10
-15
-3
0
10
-4
2
20
30
40
-4
0
Policy Rate
x 10
5
x 10
10
-6
20
Real House Prices
0
0
-5
-2
-4
Taylor Rule
Macro Prudential
Augmented Taylor Rule
0
10
20
30
-10
40
-15
0
10
20
Figure 2.9:Time Preferences Shock, Euro Area
-3
6
Credit
x 10
10
x 10
-4
Inflation
4
5
2
0
0
-2
0
10
-3
6
20
30
40
-5
0
Investment
x 10
GDP
0.02
2
0.01
0
0
0
10
-3
2
20
0.03
4
-2
10
20
30
40
-0.01
0
Policy Rate
x 10
6
x 10
10
-5
20
Real House Prices
4
1.5
2
1
0
0.5
0
Taylor Rule
Macro Prudential
Augmented Taylor Rule
-2
0
10
20
30
40
-4
0
10
20
30
40
Figure 3.1: Euro area counterfactuals implementing the optimized rules
(ignoring the ZLB on the short-term interest rate)
GDP growth
Credit growth
6
14
4
12
10
2
8
0
6
-2
4
-4
2
-6
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
0
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
Data
Taylor rule
Data
Taylor rule
Augmented Taylor rule
TR+Macroprudential
Augmented Taylor rule
TR+MAcroprudential
GDP deflator
Policy rate
3,5
8
3
6
2,5
4
2
1,5
2
1
0
0,5
0
-2
-0,5
-4
-1
-1,5
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
-6
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
Data
Taylor rule
Data
Taylor rule
Augmented Taylor rule
TR+Macroprudential
Augmented Taylor rule
TR+Macroprudential
Figure 3.2: Euro area counterfactuals implementing the optimized rules
(imposing the ZLB on the short-term interest rate)
GDP growth
Credit growth
6
14
4
12
10
2
8
0
6
-2
4
-4
-6
2000Q1
2
2002Q1
2004Q1
2006Q1
2008Q1
0
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
Data
Taylor rule
Data
Taylor rule
Augmented Taylor rule
TR+Macroprudential
Augmented Taylor rule
TR+MAcroprudential
GDP deflator
Policy rate
4
6
3
5
2
4
1
3
0
2
-1
1
-2
-3
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
0
2000Q1
2002Q1
2004Q1
2006Q1
2008Q1
Data
Taylor rule
Data
Taylor rule
Augmented Taylor rule
TR+Macroprudential
Augmented Taylor rule
TR+Macroprudential