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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Money in the utility modelMonetary Economics
Michaª Brzoza-Brzezina
Warsaw School of Economics
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Introduction
The neoclassical growth model by Ramsey (1928) and Solow
(1956) provides the basic framework for modern
macroeconomics.
But these are models of nonmonetary economies.
In order to explain monetary policy we have to introduce a
monetary policy instrument.
Sidrauski (1967) introduced money into the neoclassical
growth model.
At this time money was considered the monetary policy
instrument.
The model derives from Ramsey (1928): utility maximization
by the representative agent
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Introduction (cont'd)
How can we introduce money?
- �nd explicit role for money (e.g. money is necessary to make
transactions - Cash in Advancec (CIA) approach, Clower
(1967))
- assume money yields utility (Money in the Utility Function
(MIU) - Sidrauski (1967))-
MIU is simple though not very appealing. Can be rationalized
by transaction dedmand for money.
The model allows us studying:
- impact of money (i.e. monetary policy) on the real economy,
- impact of money on prices,
- optimal rate of in�ation.
Derivation follows (approximately) Walsh (2003)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Household's problem - objective
Suppose the utility function of the representative household is:
Ut = u(ct ,mt) (1)
where mt =MtPt
and utility is increasing and strictly concave in both arguments.
The representative household seeks to maximize lifetime utility:
U = E0∞
∑t=0
βtu(ct ,mt) (2)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Constraint
subject to the budget constraint:
yt + τt + (1− δ)kt−1 +it−1Bt−1
Pt+
Mt−1Pt
= (3)
ct + kt +Mt
Pt+
Bt
Pt
and the production function:
yt = f (kt−1)
This means that households' income can be spent on consumption,
invested as capital, saved as bonds or kept as money.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Constraint
We can substitute the production function and rewrite the budget
constraint:
f (kt−1) + τt + (1− δ)kt−1 +it−1bt−1 +mt−1
πt
= ct + kt +mt + bt (4)
where πt ≡ Pt/Pt−1.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Lagrangian
Lagrangian.
maxc,m,k,b
E0∞
∑t=0
βtu(ct ,mt) + λt [f (kt−1)
+τt + (1− δ)kt−1 +it−1bt−1 +mt−1
πt
−ct − kt −mt − bt ] (5)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
First order conditions
First order conditions for this problem are:
ct :βtu′(ct) = λt (6)
kt :−λt + Etλt+1[f
′(kt) + (1− δ)] = 0 (7)
mt :
βtu′(mt)− λt + Etλt+1
1
πt+1
= 0 (8)
bt :
−λt + Etλt+1
itπt+1
= 0 (9)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Equilibrium conditions - consumption Euler equation
FOCs can be transformed into formulae describing allocation
choices in equilibrium.
Substitute (6) into (9):
u′(ct) = βEtu′(ct+1)
itπt+1
(10)
This is the Euler equation - key intertemporal condition in general
equilibrium models. It determines allocation over time.
Utility lost from consumption today equals utility from consuming
tomorrow adjusted for the (real) gain from keeping bonds.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Equilibrium conditions - capital
Substitute (9) into (7):
Etu′(ct+1)[f
′(kt) + (1− δ)] = Etu′(ct+1)
itπt+1
(11)
De�ne real interest rate (Fisher equation):
rt ≡ Etit
πt+1
(12)
The marginal product of capital (net of depreciation) equals the
real interest rate (up to �rst order).
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Equilibrium conditions - Opportunity cost of money
Merge (6), (9) and (8):
βtu′(mt)− βtu′(ct) +βtu′(ct)
it= 0
Divide by βtand rearrange:
u′(mt)
u′(ct)= 1− 1
it(13)
This equates the marginal rate of substitution between money and
consumption to their relative price
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Technology and utility
In order to solve the model we have to assume a production and
utility function.
Production function:
yt = eztkαt−1 (14)
where
zt = ρzzt−1 + εz,t (15)
is a stochastic TFP process.
Utility function:
Ut = ln ct + lnmt (16)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Monetary policy
Monetary policy is very simple. We assume that money follows a
stochastic process:
Mt = eθtMt−1
which yields:
mt =mt−1
πteθt (17)
where:
θt = ρθθt−1 + εθ,t (18)
Is a stochastic money supply process.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Government and bonds
Bonds are in zero net supply
b = 0
and the Government budget is balanced every period
τtPt = Mt −Mt−1
in real terms
τt = mt −mt−1
πt
Combine these with the household budget constraint (4):
yt + (1− δ)kt−1 = ct + kt (19)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
The model
We now have 9 equations:
(10), (11), (12), (13), (14), (15), (17), (18), (19)
to solve for 9 endogeneous variables:
yt , kt , mt , ct , it , rt , πt , zt , θt .This will be done in two steps:
1 Find the model's steady state,
2 Derive log-linear approximation arround it.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
The steady state
The steady state is where the model economy converges to
(stabilizes) in the absence of shocks.
In our model there is no population or productivity growth, so in
the steady state output, consumption etc. will be constant.
How do we solve for the steady state?
1 assume shocks are zero,
2 drop time indices (xt−1 = xt = x ss).
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
The steady state - capital
The steady state is where the model economy converges to
(stabilizes) in the absence of shocks.
In our model there is no population or productivity growth, so in
the steady state output, consumption etc. will be constant.
From (11), (12) and (14) we have:
α (kss)α−1 =1
β− 1+ δ
or
kss =
[1
α
(1
β− 1+ δ
)] 1α−1
(20)
This determines capital (and output) in the SS.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
The steady state - consumption
Take (19) and (14). This yields:
css = (kss)α − δkss (21)
Since kss is given by (20), this determines steady state consumption.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
The steady state - money
Take (13) and (10). The former yields:
mss = cssi ss
i ss − 1
The latter
i ss = πss/β
so that:
mss = cssπss
πss − β(22)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Neutrality of money
De�nition of (long run) neutrality: in the long run real
variables do not depend on money nor in�ation.
This is true for all variables but real money holdings.
But the latter are usualy excluded from the de�nition.
So, in the MIU money is neutral in the long run.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Short run dynamics
Steady state tells us about the long run
But most interesting in most applications are short run
dynamics
We have a set of di�erence equations that describe it
But they are non-linear: di�cult to solve, to estimate etc.
The traditional way of analyzing the dynamics of a model is to
assume that the economy is always in the viscinity of the
steady state
Then we take a log-linear approximation around the steady
state which is relatively simple to handle
The procedure of log-linearization is explained in Walsh (p.
85-87)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Log-linearization
Log-linear approximation. De�ne xt ≡ ln(
xtxss
)as log-deviation
from steady state.
It follows that:
xt = x ssxtx ss
= x sse xt ' x sse0 + x sse0(xt − 0) = x ss(1+ xt)
This can be used to derive the approximation. Some useful tricks:
xtyt = x ssy ss(1+ xt + yt) (23)
xtyt
=x ss
y ss(1+ xt − yt) (24)
xat = (x ss)a(1+ axt) (25)
ln xt = ln x + xt (26)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL Euler
c−1t = βEtrt
ct+1
(css)−1 (1− ˆct) = βr ss
cssEt(1+ rt − ct+1)
Steady state;
(css)−1 = βr ss
css(27)
Divide:
ct = Et(ct+1 − rt) (28)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL budget constraint
yt + (1− δ)kt−1 = ct + kt
y ss(1+ yt) + (1− δ)kss(1+ kt−1) = css(1+ ct) + kss(1+ kt)
Steady state:
y ss + (1− δ)kss = css + kss
Substract:
y ss yt + (1− δ)kss kt−1 = css ct + kss kt
Rearrange:
kt = (1− δ)kt−1 +y ss
kssyt −
css
kssct
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL production function
yt = eztkαt−1
ln yt = zt + α ln kt−1
ln y ss + yt = zt + α ln kss + αkt−1
y ss = (kss)α
yt = αkt−1 + zt (29)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL Fisher
rt ≡ Etit
πt+1
rt ≡ ıt − Et πt+1 (30)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL money rule (money supply)
mt =mt−1
πteθt
lnmss + mt = lnmss + mt−1 − lnπss − πt + θt
mt = mt−1 − πt + θt (31)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL opportunity cost (money demand)
ctmt
= 1− 1
it
css
mss(1+ ct − mt) = 1− 1
i ss(1− it)
Steady state:
css
mss=
i ss − 1
i ss
ct − mt =it
i ss − 1
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL equilibrium condition for capital
Et
[1
ct+1
[αezt+1kα−1t + (1− δ)]
]= Et
[1
ct+1
rt
]Denote xt ≡ αeztkα−1
t−1 + (1− δ) = α ytkt−1
+ 1− δ
Et
[1
ct+1
xt+1
]= Et
[1
ct+1
rt
]x ss
cssEt(1− ct+1 + xt+1) =
r ss
cssEt [1− ct+1 + rt ]
Divide by steady state xss
css = r ss
css
Et xt+1 = rt
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL equilibrium condition for capital cont'd
Now go back to xt ≡ α ytkt−1
+ 1− δ
x ssEt(1+ xt+1) = αy ss
kssEt(1+ yt+1 − kt) + 1− δ
substitute for Et xt+1 and x ss
r ssEt(1+ rt) = αy ss
kssEt(1+ yt+1 − kt) + 1− δ
substract steady state
r ss = αy ss
kss+ 1− δ
r ss rt = αy ss
kssEt(yt+1 − kt)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
LL conclusions
Again, we have 9 equations in 9 variables,
We have some additional parameters (r ss , πss ,i ss) that haveto be calculated,
From (27): r ss = β−1
From (17): πss = 1
From (12): i ss = πssr ss = β−1
Now we can solve the model e.g. in DYNARE and check its
properties.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Simulations in DYNARE
Simulations will be done in DYNARE
What is DYNARE?
Software (free :-)) for solving, simulating and estimating
general equilibrium models
www.dynare.org
But requires a computing platform: MATLAB (expensive) or
Octave (free)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Parameters
We have to assume parameter values. These are taken from the
literature
β = .99 (Note that in steady state this is the reciprocal of the
(annualised) real interest rate of 4%)
α = .33 (Capital share)
δ = .025 (Depreciation approx. 10% on annual basis)
ρz = ρθ = .9 (Some inertia)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
IRFs to a productivity shock
10 20 30 400
0.005
0.01
0.015y
10 20 30 400
1
2
3
4x 10
−3 c
10 20 30 400
2
4
6x 10
−3 k
10 20 30 400
1
2
3
4x 10
−3 m
10 20 30 40−2
0
2
4x 10
−4 r
10 20 30 40−3
−2
−1
0
1x 10
−3 pi
10 20 30 400
0.005
0.01
0.015z
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
IRFs to a money supply shock
10 20 30 40−0.01
−0.008
−0.006
−0.004
−0.002
0m
10 20 30 400
0.002
0.004
0.006
0.008
0.01i
10 20 30 400
0.005
0.01
0.015
0.02pi
10 20 30 400
0.002
0.004
0.006
0.008
0.01
0.012theta
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
IRFs - conclusions
Productivity shocks have an impact on the real economy and
drive the business cycle in the MIU model
Monetary shocks have an impact on nominal variables:
in�ation and nominal interest rate
But monetary shocks a�ect only real money balances, but no
other real variables
In the MIU model money is neutral also in the short run.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
MIU - some exercises
Business cycle properties of the MIU model
take the linearized version of the MIU model (MIU.mod)calibrate the standard deviation and autoregression of theproductivity shock εz,t to match the respective moments forGDP in US data.see what happens if productivity zt is assumed i.i.d. Compareimpulse responses and moments with the calibration above.
Optimal rate of in�ation
take the nonlinear version of the MIU model (MIU_level.mod).Add a variable that calculates welfarecheck how steady state welfare depends on the in�ation rate
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Optimal rate of in�ation
One of the hot topics in monetary economics is the optimal
rate of in�ation.
This is not only a theoretical but also a very practical question:
Central Banks have to choose in�ation targets
What does the MIU model tell us about the optimal rate of
in�ation?
Utility depends on consumption and real money balances.
The only impact in�ation has on utility is via real money
balances.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Optimal rate of in�ation cont'd
For easiness of exposition let's restrict attention to steady
state. Look at (22)
mss = cssπss
πss − β= css(1+
β
πss − β)
We now that css is independent of in�ation. So in�ation
reduces real money balances and, hence, utility.
Optimal is minimal rate of in�ation. Given (12) and the
requirement i ≥ 0 we have:
πOPT = −r
So the optimal rate of in�ation (in the MIU model) is de�ation
at rate −r . This was postulated by M.Friedman.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Nonneutrality in the MIU model
Our model is neutral,
But it is possible to generate nonneutrality in the MIU model,
One possibility: add labour-leisure choice and nonseparable
preferences,
Walsh (2003) shows the solution and simulations,
Fig 2.3 and 2.4 from textbook,
But this model cannot, under standard calibration, generate
reaction functions and moments similar to those observed in
the data.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Applications of DSGE models
1 The MIU model is very simple, has many drawbacks and is
treated these days as a toy model
2 But is has the basic features of a dynamic stochastic general
equilibrium model
3 So, what can such models be used for?
1 explaining the past2 forecasting3 generating counterfactual simulations4 comparing alternative policies5 designing optimal policy6 and probably many others
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Explaining the past
Every endogenous variable can be decomposed into e�ects of
past shocks
To see this note, that the solution of a DSGE model is a VAR,
and a VAR can be written in MA form
But, in contrast to most VARs all shocks in a DSGE have an
economic interpretation
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Explaining the past - example
Role of �scal shocks during the �nancial crisis (Coenen,
Straub, Trabandt, 2011; AER)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Explaining the past - example cont'd
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Forecasting
Likewise, a VAR can be used to forecast
We only have to make assumptions about future shocks
Unconditional forecast - all future shocks assumed to be zero
Conditional forecast - some shocks assumed for future periods
The crucial assumption is whether these are expected or
unexpected
Expected shocks have an impact before they arrive
This often gives counterintuitive results (see e.g. Laseen &
Svensson, 2011; IJCB)
Several central banks developped forecasting DSGE models:
SIGMA (Fed), Ramses (Riksbank), Nemo (Norges Bank),
NAWM (ECB), SOE.PL (NBP)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Forecasting - example
GDP growth forecast - NBP (Kªos, 2014)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Counterfactual simulations
DSGE models are robust (???) to the Lucas Critique
So, they can be used to simulate di�erent policies
In general, two possible types of counterfactual simulations
change some shocks in the pastchange policy in the past
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Counterfactual simulations - shocks
Role of �nancial shocks during the crisis (Brzoza-Brzezina &
Makarski 2011, JIMF)
0 10 20 30 40 50 60 70−0.03
−0.02
−0.01
0
0.01
0.02
0.03
data & forecastcounterfactual scenariodifference
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Counterfactual simulations - policy
Role of independent monetary policy during the crisis
(Brzoza-Brzezina, Makarski, Wesoªowski 2014, EM)
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Designing optimal / comparing alternative policies
DSGE models have a natural metric for optimality
This is houesehold welfare
E.g. see our discussion of optimal in�ation rate
More complicated experiments can be done numemericaly
But, optimal policy can have a very complicated design
Instead of looking for optimal policy we can compare how
close to optimal implementable policies are (e.g. Taylor rules)
We can look for robust policies
In particular we can test new policies (with no history to use
econometrics), e.g. macropru, ZLB
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Comparing alternative policies - example
Macroprudential policy in a monetary union (Brzoza-Brzezina,
Kolasa, Makarski 2015, JIMF)
0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 1.1 1.12 1.140
0.5
1
1.5
2
2.5
3
Std. of output in periphery
Std
. of c
redi
t in
perip
hery
Ind. monet., no macropru.Union, no macropru.Union, ind. macropru. frontierUnion, common macropru.Union, aggressive monet.
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
Plan of the Presentation
1 Motivation
2 Model
3 Steady state
4 Short run dynamics
5 Simulations
6 Extensions
7 Conclusions
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Motivation Model Steady state Short run dynamics Simulations Extensions Conclusions
MIU - conclusions
We solved and simulated a simple monetary dynamic
stochastic general equilibrium model (DSGE),
This was able to re�ect some desired business cycle features in
reaction to productivity shocks,
But monetary shocks have only nominal e�ects (except for
impact on real money balances),
This is inconsistent with stylised facts presented in Lecture 1,
We have to look for other models,
E.g. the sticky price new Keynesian model - next lecture.
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