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Motivation Model Simulations and properties Applications Extensions and summary
The new Kenesian model
Michaª Brzoza-Brzezina
Warsaw School of Economics
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Motivation Model Simulations and properties Applications Extensions and summary
Plan of the Presentation
1 Motivation
2 Model
3 Simulations and properties
4 Applications
5 Extensions and summary
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Motivation Model Simulations and properties Applications Extensions and summary
Introduction
The MIU model was not able to re�ect features of realeconomies: lagged and prolonged reaction to shocks
The MIU model showed full neutrality and superneutrality ofmoney
Moreover central banks have generally moved from controlingmonetary aggregates to controlling short-term interest rates.
We need better models that can reproduce the basic featuresof the economy and inttroduce interest rates as monetarypolicy instrument.
The New Keynesian Model goes in this direction
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Motivation Model Simulations and properties Applications Extensions and summary
Introduction (cont'd)
It is the standard workhorse model of today's macroeconomistsanalyzing monetary policy and business cycles.
Origin:- The NKM is a general equilibrium model (as MIU) and isbased on the principle of microbased optimization. This can betraced back to the Lucas Critique and Real Business Cycle(RBC) economics of the 1980's and to the MIU model.- The standard MIU/ RBC model has been modi�ed in twoways:1) the old postulate of the Keynesian school that nominalrigidities matter was incorporated2) the idea that central banks adjust interest rates (and notmoney) in reaction to deviations of in�ation and output fromtargets (Taylor 1993) was incorporated.
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Motivation Model Simulations and properties Applications Extensions and summary
Introduction (cont'd)
The NKM is the best (or at least most popular) we have, butwe should be aware of its weaknesses. Economist constantlywork on development of new models.
E.g. models with �nancial frictions, banking sector, labormarket etc.
The derivation follows (approximately) Gali (2008)
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Motivation Model Simulations and properties Applications Extensions and summary
Plan of the Presentation
1 Motivation
2 Model
3 Simulations and properties
4 Applications
5 Extensions and summary
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Motivation Model Simulations and properties Applications Extensions and summary
Di�erence to MIU
Three basic modi�cations with respect to the MIU model:
1 ignore endogenous capital adjustment as suggested byMcCallum and Nelson (1999): it does not matter much for theanalysis of business cycle �uctuations
2 incorporate di�erentiated goods produced by monopolisticallycompetitive �rms (Dixit and Stiglitz 1977) facing constraintsto price adjustments (Calvo 1983)
3 represent monetary policy as setting the nominal interest ratein reaction to deviations of in�ation and output from targets
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Motivation Model Simulations and properties Applications Extensions and summary
Framework
Households consume and provide labour services, save inone-period bonds
Final good producers operate under perfect competition andproduce consumption good from intermediate goods
Intermediate goods producers operate under monopolisticcompetition and produce di�erentiated intermediate goods
Central bank sets interest rates (Taylor rule)
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Motivation Model Simulations and properties Applications Extensions and summary
Household's problem - objective
maxU = E0∞
∑t=0
βt [c1−σt
1− σ− n
1+ϕt
1+ ϕ] (1)
where nt is the work e�ort and ct is the �nal consumption good,subject to
Ptct + Bt = Wtnt + Rt−1Bt−1 + Tt
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Motivation Model Simulations and properties Applications Extensions and summary
FOCs
Lagrangean:
L = E0∞
∑t=0
[βt [c1−σt
1− σ− n
1+ϕt
1+ ϕ] +
+λt
Pt(Wtnt + Rt−1Bt−1 + Tt − Ptct − Bt)]
First order conditions are:
ct : c−σt = λt (2)
Bt
Pt: −λt + Etβλt+1Rt
Pt
Pt+1
= 0 (3)
nt : −nϕt + λt
Wt
Pt= 0 (4)
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Motivation Model Simulations and properties Applications Extensions and summary
Household's equilibrium conditions
FOCs yield two equilibrium conditions.Intertemporal - choice between consumption today and tomorrow:
c−σt = Etβc−σ
t+1Rt
Pt
Pt+1
(5)
Intratemporal - choice between consumption and leisure (laborsupply):
nϕt
c−σt
=Wt
Pt≡ wt (6)
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Motivation Model Simulations and properties Applications Extensions and summary
Household's equilibrium conditions LL
FOCs yield two equilibrium conditions.Intertemporal - choice between consumption today and tomorrow:
ct = Et ct+1 −1
σ
(Rt − Etπt+1
)(7)
Intratemporal - choice between consumption and leisure (laborsupply):
ϕnt + σct = wt (8)
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Motivation Model Simulations and properties Applications Extensions and summary
Final good producers
The �nal good producers combine di�erentiated intermediate goodsinto one �nal consumption good. They act under perfectcompetition and solve:
maxPtyt −1∫
0
Pj ,tyj ,tdj (9)
subject to:
yt =
[∫1
0
yε−1
εj ,t dj
] εε−1
(10)
where ε can be thought of as elasticity of substitution between thegoods yj . The higher is ε the better substitutes are these goods.
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Motivation Model Simulations and properties Applications Extensions and summary
FOCs
Lagrange'an:
Lt = Ptyt −∫
1
0
Pj ,tyj ,tdj − λt
[∫
1
0
yε−1
εj ,t dj
] εε−1
− yt
(11)
FOC:
yj ,t : Pj ,t − λtε
ε− 1
[∫1
0
yε−1
εj ,t dj
] εε−1−1
ε− 1
εy
ε−1ε −1
j ,t
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Motivation Model Simulations and properties Applications Extensions and summary
Demand function
Solves to (details in the technical appendix):
yj ,t =
(Pj ,t
Pt
)−ε
yt
where:
Pt =
1∫0
P1−εj ,t dj
11−ε
is the aggregate price level.
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Motivation Model Simulations and properties Applications Extensions and summary
The intermediate goods �rms
The intermediate goods production process is where thenominal stickiness is introduced.
In particular the standard assumption in the NK model is thatprices are sticky.
Some �rms will not be able to adjust their prices.
This is the reason we assume products are di�erentiated.
Under perfect competition everybody has the same price(equal to marginal cost).
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Motivation Model Simulations and properties Applications Extensions and summary
General setting
There is an in�nite number of �rms of measure one
Each produces a unique good according to the followingtechnology: yt(ι) = atnt(ι).
When setting its price the �rm is subjct to a rigidity: eachperiod only a fraaction 1− θ of �rms are allowed to changetheir prices.
The �rm's problem will be solved in two stages
First choose optimal factor employment to minimize theproduction cost.
Next, choose optimal price to maximize pro�ts.
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal factor employment
The (real) cost function is qt = wtnt(ι)
Lagrangean: Lt = wtnt(ι) + µt (yt(ι)− atnt(ι))
First order condition wrt. nt(ι):wt = µtat
Substitute into cost function: qt(ι) = µtatnt(ι) = µtyt(ι)
Real marginal cost is: mct(ι) ≡ δqt (ι)δyt (ι)
= µt =wtat
Note that the RHS is independent of ι. Hence, so is the LHS.The marginal cost is the same for every �rm.
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Motivation Model Simulations and properties Applications Extensions and summary
Log-linearization
Marginal cost: mct =wtat
Log-linear approximation: mc(1+ ˆmct) =wa (1+ wt − at)
Steady state: mc=wa
Divide to get: mct = wt − at
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal price setting
This is somwhat more complicated. Assume that every period onlya fraction 1− θ of �rms are allowed to change their prices. The�rm maximizes expected lifetime pro�ts:
maxΠt = Et
∞
∑i=0
(βθ)iΛt,t+i
(P∗t (ι)
Pt+i−mct+i
)yt+i (ι)
subject to the demand functions of �nal good producers:
yt+i (ι) =
(P∗t (ι)
Pt+i
)−ε
yt+i
where P∗t is the price set by �rms that are allowed to reoptimize inperiod t. Note that �rms are owned by households. Hence, theirpro�ts are discounted with β and vauled according to thehousehold's marginal utility of consumption:
Λt,t+i ≡u′(ct+i )
u ′(ct)20 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Price as mark-up
After some transformation (details in the technical appendix) wearrive at:
Et
∞
∑i=0
(βθ)iΛt,t+i
(P∗tPt+i
− ε
ε− 1mct+i
)y ∗t+i = 0
Note that under �exible prices (θ = 0) and monopolisticcompetition the price chosen in period t is set as a mark-up overnominal marginal cost:
P∗t = MPtmct
where M ≡ εε−1 is the gross markup.
Hence, under monopolistic competition the price is set as amark-up over marginal cost.
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Motivation Model Simulations and properties Applications Extensions and summary
Phillips curve
Further derivations bring us to the (log-linearized) new KeynesianPhillips curve
πt =(1− βθ)(1− θ)
θmct + βEt πt+1
In�ation depends on marginal cost and expected in�ation.The latter because �rms have to be forward looking. They do notknow when they will be able to reset prices.
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Motivation Model Simulations and properties Applications Extensions and summary
Monetary policy
To complete the model we have to decide upon monetary policyThe standard assumption is that it follows a Taylor ruleThis is motivated by empirical observations of central bank behavior
Rt = ρRt−1 + (1− ρ)(φππt + φy yt) + ε i ,t
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model (large version)
We now have a complete model:
Euler equation ct = Et ct+1 − 1
σ
(Rt − Etπt+1
)Consumption-leisure choice: ϕnt + σct = wt
Marginal cost: mct = wt − at
Productivity: at = ρaat−1 + εa,t
Production function: yt = at + nt
Phillips curve: πt =(1−βθ)(1−θ)
θ mct + βEt πt+1
Taylor rule: Rt = ρRt−1 + (1− ρ)(φππt + φy yt) + ε i ,t
Market clearing: ct = yt
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Motivation Model Simulations and properties Applications Extensions and summary
Large version - comments
The large version is a good starting point for applied (say atcentral banks) DSGE models
Of course it still lacks many aspects of reality
Several additional elements are added to make them matchthe data better
On the other hand, for educational reasons the NK model isoften reduced to three equations
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model (compact version)
Derivation in Gali (2008), ch. 3
The three-equation model:
πt = κxt + βEt πt+1 + εc,t (12)
xt = Et xt+1 −1
σ
(Rt − Etπt+1
)+ εa,t (13)
Rt = ρRt−1 + (1− ρ)(φππt + φy xt) + εR,t (14)
where xt denotes the (welfare relevant) output gap and
κ ≡ (1−βθ)(1−θ)θ (σ + ϕ)
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Motivation Model Simulations and properties Applications Extensions and summary
Plan of the Presentation
1 Motivation
2 Model
3 Simulations and properties
4 Applications
5 Extensions and summary
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model - demand shock
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model - supply shock
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model - monetary policy shock
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Motivation Model Simulations and properties Applications Extensions and summary
Important properties
Short-run non-neutrality condition is ful�lled
The (hybrid) NK model generates hump-shaped impulseresponse functions as evidenced in empirical studies
In reaction to demand shocks output and in�ation moove inthe same direction
In reaction to supply shocks output and in�ation moove inopposite directions
Taylor rule guarantees stability only if Taylor principle ful�lled(φπ > 1).
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Motivation Model Simulations and properties Applications Extensions and summary
Exercises (3eq NK model)
Use NKM.mod
Check the Taylor principle in practice (try φπ < 1)
How does a demand shock work? Check correlation of outputand in�ation when the standard deviation of the demand shockis increased.
Do the same for the supply shock.
How does the economy react to monetary policy when pricesare highly elastic?
What happens when they are very sticky?
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Motivation Model Simulations and properties Applications Extensions and summary
Plan of the Presentation
1 Motivation
2 Model
3 Simulations and properties
4 Applications
5 Extensions and summary
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Motivation Model Simulations and properties Applications Extensions and summary
The NK model - possible applications
The NK model (usualy somewhat extended) is used at centralbanks and academia
It o�ers several attractive applications
Examples:
Historical decompositionsForecastingCouterfactual simulationsOptimal policyRules vs. discretion
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Motivation Model Simulations and properties Applications Extensions and summary
Explaining the past (historical decompositions)
Every endogenous variable can be decomposed into e�ects ofpast 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 aneconomic interpretation
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Motivation Model Simulations and properties Applications Extensions and summary
Explaining the past - example
Role of �scal shocks during the �nancial crisis (Coenen,Straub, Trabandt, 2011; AER)
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Motivation Model Simulations and properties Applications Extensions and summary
Explaining the past - example cont'd
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Motivation Model Simulations and properties Applications Extensions and summary
Forecasting
A DSGE model (like a VAR) can be used to forecast
We 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 orunexpected
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 Simulations and properties Applications Extensions and summary
Forecasting - example
GDP growth forecast (Kªos, 2016)
16:2 16:4 17:2 17:4 18:2 18:4 19:2 19:4-1
-0.5
0
0.5
1
1.5
Wkła
d zab
urzeń [
pkt. p
roc.]
stan ustalonyzagraniczne, globalne i marż w hzsektora publicznegorynku pracymonetarnekursu walutowegokonsumpcji prywatnejinwestycji prywatnychinne podaży
-1
-0.5
0
0.5
1
1.5
PKB
- linia
ciągła
[%]
PKB
39 / 64
Motivation Model Simulations and properties Applications Extensions and summary
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 Simulations and properties Applications Extensions and summary
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 Simulations and properties Applications Extensions and summary
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 howclose 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 useeconometrics), e.g. macropru, ZLB
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Motivation Model Simulations and properties Applications Extensions and summary
Designing optimal / comparing alternative policies
Optimal monetary and macroprudential policy in the euro area(Bielecki, Brzoza-Brzezina, Kolasa, Makarski 2017)
98 00 02 04 06 08 10 12 14 161
1.1
1.2
1.3GDP in periphery
98 00 02 04 06 08 10 12 14 161
1.05
1.1
1.15GDP in core
98 00 02 04 06 08 10 12 14 161
2
3
4Credit in periphery
98 00 02 04 06 08 10 12 14 161
1.2
1.4
1.6
1.8Credit in core
98 00 02 04 06 08 10 12 14 160.4
0.6
0.8
1LTV in periphery
98 00 02 04 06 08 10 12 14 160.65
0.7
0.75
0.8
0.85LTV in core
98 00 02 04 06 08 10 12 14 16-0.1
-0.05
0
0.05Net exports in periphery
98 00 02 04 06 08 10 12 14 161
1.005
1.01
1.015Interest rate
counterfactual historical
43 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Optimal rate of in�ation (long run)
In the MIU model optimal in�ation rate is −r ss .This is the rate that maximizes money balances subject to theZLB
In the NK model this motive is absent
The argument is di�erent: there are two distortions in themodel - monopolistic competition and price dispersion
The former cannot be eliminated by monetary policy (we do itwith taxes)
But the latter can be eliminated with monetary policy
Zero in�ation makes all prices optimal
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Motivation Model Simulations and properties Applications Extensions and summary
Price dispersion
Labor market clearing:
nt =∫
1
0
nt(i)di
Substitute from production function and demand equation
nt =∫
yt (i)
atdi =
ytat
∫ (Pt (i)
Pt
)−ε
di
yt =atnt∫ (Pt (i)Pt
)−εdi≡ atnt
∆H,t
where ∆t denotes price dispersion.
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal ss rate of in�ation cont'd
Price dispersion is lowest when all prices are equal
This happens with zero in�ation
This is the optimal ss in�ation rate in the NK model
To see it we need a nonlinear model with a properly calculatedsteady state
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Motivation Model Simulations and properties Applications Extensions and summary
Welfare consequences of non-zero ss in�ation
Welfare loss is usually reported �in per cent of lifetimeconsumption�
Compare welfare of non-zero ss in�ation to welfare withπss = 0.
W alternative policy = W ss,baseline policy ((1+ x)css , nss)
W alternative policy =1
1− β
(((1+ x)css)1−σ
1− σ− (nss)1+ϕ
1+ ϕ
)
x =1
css
(1− σ)
[(1− β)W alternative policy +
(nss)1+ϕ
1+ ϕ
] 11−σ
− 1
47 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Exercise - optimal rate of in�ation
Find numerically the optimal steady state in�ation rate in theNK model
Use NKM_nonlinear.mod
This is the NK model in original (nonlinear) form, derivationscan be found in NKM_derivation.pdf
Add welfare to the model
Compare steady state welfare for various steady state in�ationrates
Show that welfare is maximized for zero steady state in�ation
Plot price dispersion as a function of steady state in�ation
Calculate the welfare loss (as % of lifetime consumption) fromannual 4% steady state in�ation
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal monetary policy (short run)
If sticky prices are the only distortion then optimal monetarypolicy in the short run is to stabilize in�ation perfectly
But in the linearized model price dispersion disappears
To speak about optimal in�ation we need a higher orderapproximation
Woodford showed that second order approximation is su�cient
Attention: there may be other distortions, e.g. sticky wages
Then optimal policy becomes more complicated (e.g. it mayalso have to stalbilize wages).
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Motivation Model Simulations and properties Applications Extensions and summary
Exercise - optimal monetary policy
Show that stable in�ation is the optimal monetary policy inthe NK model
Use NKM_nonlinear.mod
Solve for optimal monetary policy (use command�Ramsey_policy�) with welfare being the planer's objective(see Dynare User Guide for details)
Compare the outcome to the policy that fully stabilizesin�ation (you can substitute the Taylor rule with πt = 1.
Note 1: given the way ramsey_policy is declared in Dynare,period utility and not welfare is the objective
Note 2: when you compare with stable in�ation policy youhave to look at the second order approximation to the model!
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Motivation Model Simulations and properties Applications Extensions and summary
Rules (commitment) vs. discretion debate
Old debate:
should monetary policy be bound by rules or should it be freeto do whatever it wants every period?Kydland & Prescot (1980) and Barro & Gordon (1983) showthat central bank pursuing an overly ambitious output goal willend up with in�ation biasagents know that the central bank prefers high output(positive gap) and adjust expectationsas a result in�ation is higher, but output at natural levelthus CB should credibly commit to keeping output at potential
Today:
we do not think of central banks as trying to keep permanentlypositive output gapsbut Clarida, Gali & Gertler (1999) show that even withoutsuch targets, commitment can be good
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Motivation Model Simulations and properties Applications Extensions and summary
Rules (commitment) vs. discretion debate
Back to optimal policyIn the simplest case of one distortion (price dispersion) zeroin�ation is optimalBut under more general assumptions optimal policy has tosolve trade o�sRotemberg & Woodford (1998) show that when realimperfections are present the second order approximation tosocial welfare is
W0 = E0
{∞
∑t=0
βt(π2t + λx2t
)}(15)
Trade-o� between between stabilizing in�ation and output gapThis is also consistent with behavior of central banks, who aimto stabilize both in�ation and output gapsIn this case the question arises whether policy should beconducted discretionary or under commitment 52 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Optimal policy under discretion
Under optimal discretionary policy (ODP) the CB is not ableto in�uence expectations about future policy
Hence, optimizing boils down to solving static problems:
minπt ,xt ,it
1
2(π2
t + λx2t )
subject to (12) and (13).
Note that expectation terms are taken as given, since the CBis assumed not to in�uence them.
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal policy under discretion
FOCs:
πt : πt + µ1,t = 0
xt : λxt − κµ1,t + µ2,t = 0
it : µ2,t = 0
This yields
πt = −λ
κxt
This is called targeting rule (in contrast to instrument rules)After an in�ationary shock the CB allows the output gap tobecome negative 54 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Optimal policy under commitment
Under (credible) commitment the CB is able to in�uenceexpectations about future policy
Hence, it minimizes
minπt ,xt ,it
1
2E0
∞
∑t=0
βt(π2t + λx2t )
subject to current and future period (12) and (13)
Lagranean:
E0∞
∑t=0
βt{1
2(π2t + λx2t ) + µ1,t (πt − κxt − βπt+1 − εc,t ) + µ2,t
(xt − xt+1 +
1
σ(Rt − πt+1)− εa,t
)}
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Motivation Model Simulations and properties Applications Extensions and summary
Optimal policy under commitment (today's FOCs)
FOCs for t = 0:
π0 : π0 + µ1,0 = 0
x0 : λx0 − κµ1,0 + µ2,0 = 0
i0 : µ2,0 = 0
This yields
π0 = −λ
κx0 (16)
Same as under discretion56 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Optimal policy under commitment (future FOCs)
FOCs for t ≥ 1:
πt : βπt + βµ1,t − βµ1,t−1 +β
σµ2,t−1 = 0
xt : βλxt − βκµ1,t + βµ2,t − µ2,t−1 = 0
it : µ2,t = 0
This yields
πt = −λ
κ(xt − xt−1) (17)
Di�erent than in period t = 0Takes past developments into account.
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Motivation Model Simulations and properties Applications Extensions and summary
Commitment and time inconsistency
So optimal commitment policy (OCP) means doing smthtoday and promising to do smth di�erent from tomorrow on
But tomorrow will be today tomorrow!
OCP is time inconsitent
Possible solutions?
1 appoint very credible central bankers
2 or act in �timeless perspective� - pretend that OCP has beenapplied long ago and use (17) even in t = 0
Note that the time inconsistency problem is similar to the oneof forward guidance under a ZLB
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Motivation Model Simulations and properties Applications Extensions and summary
Commitment vs discretion
What is better: OCP or ODP?
Walsh shows that neither invokes an in�ation bias
But ODP generates a stabilization bias (the economy is morevolatile and welfare lower)
We'll show it numerically
The superiority of commitment calls for a credible, long-termarrangement for the central bank
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Motivation Model Simulations and properties Applications Extensions and summary
Commitment vs discretion
1 2 3 4 5 6 7 8 9 10−6
−4
−2
0x 10
−3 x
1 2 3 4 5 6 7 8 9 10−5
0
5
10x 10
−3 pi
1 2 3 4 5 6 7 8 9 10−8
−6
−4
−2
0x 10
−3 x
1 2 3 4 5 6 7 8 9 100
0.002
0.004
0.006
0.008
0.01pi
60 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Exercise - commitment vs. discretion
Show numerically that ODP generates a less stable economyand lower welfare than OCP
Use 3-eq NK model NKM.mod
Switch o� output and in�ation persistence and shockautocorrelation
Add welfare approximation to the model
Substitute Taylor rule with targeting rules for ODP or OCP(timeless)
Compare impulse responses to supply shocks
Compare volatilities and mean welfare
61 / 64
Motivation Model Simulations and properties Applications Extensions and summary
Plan of the Presentation
1 Motivation
2 Model
3 Simulations and properties
4 Applications
5 Extensions and summary
62 / 64
Motivation Model Simulations and properties Applications Extensions and summary
The NK model - possible extensions
The NK model misses several aspects of reality
Some standard extensions introduced to applied NK models:
habits in the utility function add a backward looking term tothe IS equationindexation in the price-setting process adds a backward lookingterm in the Phillips curveinvestment adjustment costs soften the response of investmentto shocks
More complicated extensions:
�nancial frictions (Bernanke, Gertler, Gilchrist 1999, Kiyotaki& Moore (1997), Iacoviello 2005)unemployment (Gali 2010)
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Motivation Model Simulations and properties Applications Extensions and summary
Summary
The new Keynesian model is a state-of-the-art DSGE model
In addition to microfoundations it features monopolisticcompetition and sticky prices
Monetary policy a�ects the real economyZero is the optimal in�ation rateCommitment policy is better than discretion
Widely used at central banks and in academic literature
64 / 64