macroeconomic experiments charles noussair tilburg university barcelona, june 14, 2015
TRANSCRIPT
Outline of this lecture
• This talk is divided into three segments: – (1) Institutions and Growth– (2) Asset Market Bubbles – (3) DSGE Economies
• I will describe three lines of research. The idea is that they may stimulate research ideas on your part.
The experimental approach to studying models
Outcomes
Behavior of Agents
Structure of Economy (objectives, constraints, rules)
The experimenter specifies the structure of the economy, and observes behavior and outcomes.
Theoretical models specify structure and behavior, and study outcomes
Piece “A” is sometimes easy (desirable), and sometimes not
Theory Experiment
Asssumption on Rationality, Expectations
?
Assume structure
Create structure
Equilibria, simulated Outcomes
DataCompare
A
Main source of hypotheses
Some features may not be feasible, some may not be sensible
Economic Model
Part I: Growth and Institutions
• THE STRUCTURE OF THE MODEL• A representative consumer in the economy has a lifetime utility given by:
• ρ is the discount rate, Ct is the quantity of consumption at time t, and U(Ct) is the utility of consumption. The economy faces the resource constraint:
• δ is the depreciation rate, Kt is the economy’s aggregate capital stock at the beginning of period t, and A is an efficiency parameter on the production technology. The production function is concave.
• THE ASSUMPTION ON BEHAVIOR: The agent maximizes lifetime utility• • OUTCOME OF MODEL: the principal result of the model is that Ct and Kt converge
asymptotically to optimal steady state levels.
• The optimal steady state given by the solution to: C* = F(K*) – δK* and K* = ρ + δ
)()1(0
t
tt CU
tttt KKFAKC )1()(1
If individuals are given incentives to solve the dynamic optimization problem on their own, it is very difficult.
Social Planners
Figure 6: Time Series of Consumption: Social Planners High Endowment
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Time
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A1
A2
C1
C2
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C*
Suppose a team of five people is making the decision instead. They do better but still have a lot of trouble
Implement model as a Decentralized Economy.
• There are five agents in the economy• The economy’s production capability and utility function is divided up
among the five agents.• Agents are made asymmetric.• A market is available to exchange capital (using double auction rules,
because a competitive model is being tested).• There is money, an experimental currency, in the economy, which agents
use for purchases and sales of capital.
Timing within a period t
• At the beginning of period t, production occurs mapping kt into output (ct + kt+1)
• A double auction market for output is open for two minutes in which they can exchange output.
• Agents have one minute to allocate any portion of their output to consumption ct
• At the beginning of period t+1, production occurs mapping kt+1 into output (ct+1 + kt+2).
Timing of sessions (ending a session)
• A horizon refers to the entire life of an economy.• Implementation of infinite horizon with discounting: In each
period, there was a /(1+ ) probability that the horizon would end. • If a horizon ended with more than one hour to go in the
experimental session, a new horizon was started.• If a horizon still had not ended at the scheduled end of the session,
the horizon would be continued on another evening.• Subjects would have the option of continuing in their roles in the
continued session. • If they chose not to continue, a substitute would be recruited to
take her place. The original subject would also receive the money earned by the substitute.
Results: Consumption patterns in the decentralized economy
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time
Cons
umpti
on C2E2F3G4C*
C*=12
K*=10
Ko =20
An environment with multiple equilibria
• Now suppose that there exist two stable equilibria, which are Pareto-ranked so that the inferior equilibrium represents a poverty trap.
• The value of the productivity parameter A depends on the economy’s capital stock. There exists a threshold level of capital stock, above which A has a higher value.
KKA
KKAA
ˆ if ,
ˆ if ,
Production function includes threshold externality
Aggregate Production Function
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Units of Input
Out
put
Theoretical Predictions for data in the following slides
• There is an optimal steady state in which (C*, K*) = (70,45) • From any initial level of capital stock, optimal decisions (of a benelovent social
planner) at each point in time imply monotonic convergence to (C*, K*).• However, if the economy is decentralized, there are two stationary rational
expectations competitive equilibria at (CH, KH, pH) = (70,45,118) and (CL, KL, pL) = (16,9,334)
• RESULT: The decentralized economy converges to the poverty trap.
Results: Observed and Equilibrium Aggregate Consumption (Five Sessions)C* optimal = 70, C* poverty trap = 16
Em ory B1
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60
80
Ti m e
Cons
umpt
ion
Da t a C* o p t i m a l C* i n f e r i o r
Em ory B2
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40
60
80
Ti m e
Cons
umpt
ion
Da t a C* o p t i m a l C* i n f e r i o r
Em ory B3
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40
60
80
Ti m e
Cons
umpt
ion
Da t a C* o p t i m a l C* i n f e r i o r
Cal t ech B1
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60
80
Ti m e
Cons
umpt
ion
Da t a C* o p t i m a l C* i n f e r i o r
Cal t ech B2
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20
40
60
80
Ti m e
Cons
umpt
ion
Da t a C* o p t i m a l C* i n f e r i o r
§ = E a c h d a t a p o i n t r e p r e s e n t s a p e r i o d i n a h o r i z o n . H o r i z o n s a r e s e p a r a t e d b y s p a c e s
Results:
No economy surpasses the capital stock threshold.
Convergence to near poverty trap is typical outcome
Vertical axes: aggregate consumption
Horizontal axes: time
Breaks in series: New horizon beginning
The Communication treatment
– Identical to the baseline treatment, except that before the market opened, subjects were allowed to communicate with each other.
– Each agent’s screen displayed a chat-room, which they could use to send and receive messages in real time.
– Communication was unrestricted and all agents could observe all messages.
Observed and Equilibrium Aggregate Consumption, Communication Treatment; C* optimal = 70, C* inferior = 16
Em or y C1
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2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Em or y C2
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Em or y C3
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Ca l t ec h C1
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Ca l t e c h C2
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2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Ca l t e c h C3
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2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Results: Individual sessions converge to near one of the equilibria.
However, which equilibrium it converges to varies between sessions.
Example of how institutional structure affects mean and variance of income.
Vertical axes: aggregate consumption
Horizontal axes: time
Breaks in series: New horizon beginning
The Voting treatment– Identical to the baseline treatment except that consumption and
investment decisions were determined in the following manner:– Two agents were randomly chosen in each period to make
proposals on how much each agent in the economy should consume.
– Before submitting proposals, proposers received information indicating the current stock of capital held by each agent.
– Proposals were followed by majority voting. All agents were required to vote in favor of exactly one of the two proposals.
– The proposal that gained at least 3 (of the 5 total) votes became binding. Each agent consumed the quantity of output specified under the winning proposal, and began next period with the amount of capital allotted to her under the winning proposal.
Observed and Equilibrium Aggregate Consumption, Voting Treatment
C* optimal = 70, C* inferior = 16
Em or y V1
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2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Em or y V2
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2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Em or y V3
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Ca l t ec h V1
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Ca l t e c h V2
0
2 0
4 0
6 0
8 0
Ti m e
Cons
umpt
ion
Data C* o p timal C* in ferio r
Results:
-In most sessions, economy escapes poverty trap
- High variance from one period to the next within sessions.
- Convergence toward equilibrium typically does not occur
Vertical axis: aggregate consumption
Horizontal axis: time
Breaks in series: New horizon beginning
Observed and Equilibrium Aggregate Consumption, Hybrid Treatment; C* optimal = 70, C* inferior = 16
H yb r i d ( E m o r y s e s s io n 1 )
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T im e
Ag
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D a ta C * o p t im a l C * in fe rio r
H yb r i d ( Em o ry s e s s io n 2 )
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H y b r id (Em o r y s e s s io n 3 )
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H yb r id (C a lt e c h s e s s i o n 1 )
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T im e
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D a ta C * o p tim a l C * in fe r io r
H yb r id ( C a l t e c h s e s s io n 2 )
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Result; The addition of voting and communication allows the economy to escape poverty trap in all sessions.
Vertical axis: aggregate consumption
Horizontal axis: time
Breaks in series: New horizon beginning
Results• Baseline: The economies of the baseline treatment converge
to near the poverty trap. Does not escape poverty trap in any session.
• Communciation: The economies of the communication treatment converges to close to one of the stationary equilibria. However, the one it converges toward varies between sessions. Probability of avoiding the poverty trap greater than under baseline.
• Voting: The voting treatment exhibits variable behavior from one period to the next. Probability of avoiding the poverty trap greater than under baseline.
• Hybrid: Also shows variable behavior from one period to the next. Escapes the poverty trap in all sessions.
Part 2: Bubbles and Crashes• I will discuss work on experimental markets
for long-lived assets. • In such markets, the bubble and crash pattern
in pervasive.• I will first describe the effect of different
market institutions and parameters on bubble formation
• Then I will concentrate on differences between sessions but within treatments.
The type of asset considered: the type first studied by (Smith, Suchanek Williams, 1988)
• The asset has a life of 15 periods. At the end of each period, the asset
pays a dividend which is equally likely to be 0, 8, 28 and 60 cents,
determined independently for each draw.
• After the last dividend is paid, the asset has no value.
• Assuming traders are risk neutral, the fundamental value of this asset
can be calculated at any point in time. It is equal to the expected total
future flow of dividends.
• Since the expected dividend in any dividend draw is equal to 24, the
fundamental value is equal to the number of dividend draws remaining
times 24.
• Therefore, the fundamental value is 360 at the beginning of the life of
the asset, and declines by 24 cents every period.
What happens in such a market?
b) Benchmark
Rather than tracking the fundamental value, a bubble and crash pattern is typically observed.
Decreasing fundamental value trajectory
Decreasing treatment tracks fundamentals more closely than increasing
Modeling trader types• Can we explain what is observed in the data with a model in
which there are multiple trader types?• Consider the following model (based on DeLong, Shleifer,
Summers, and Waldmann ,1990)• Assume three types of trader: (1) Fundamental value traders,
(2) Rational Speculators, and (3) Momentum traders.– Fundamental value traders purchase if prices are below fundamentals
and sell if they are above. – Rational speculators anticipate future price movements. They buy if
the price is going to increase in the next period, and sell if it is going to decrease.
– Momentum traders follow the current trend. They buy if the price has been going up and sell if it has been going down.
Demand functions of the three types
• The three types have net demand functions of the following form:– Fundamental value trader: -α(pt – ft), where pt is the price
in period t, and ft is the fundamental value in period t. – Rational speculator: γ(E(pt+1) - pt), – Momentum trader: -δ + β(pt-1 – pt-2)
• Six parameters: α, β, γ, δ, and the proportion of traders that is of each type.
• This structure generates bubbles and crashes, is consistent with treatment differences, and the types correlate with other behaviors.
• Now I will explore the relationship between trader characteristics and market behavior.
Cognitive reflection test (Frederick, 2005)• This consists of three questions, in which the first answer that comes to mind is incorrect,
but the correct answer is simple after some reflection.
• Example: If it takes 5 people 5 minutes to make 5 units, how much time does it take 100 people to make 100 units?– First answer that comes to mind is 100 minutes– With reflection realize the answer is 5 minutes
• Other two questions:• A bat and a ball cost 1.10 Euro in total. The bat costs 1 Euro more than the ball. How much
does the ball cost?• In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48
days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?
Emotional correlates of asset price movements
• Positive emotions have typically been associated with higher prices– Irrational exuberance (Greenspan, 1996)– Speculative euphoria (Galbraith, 1984)
• Empirical support– Weather affects returns (Hirshleifer and Shumway, 2003; Kamstra et al, 2003)– Outcomes of sporting events also affect prices (Edmans et al., 2007)– Twitter mood (Bollen et al., 2010) and anxiety level in blog postings (Gilbert
and Karahalios, 2009) predict stock price movements.
• Fear has been associated with low prices and volatility
Emotions and markets in the laboratory
• We consider whether these and other connections between emotions and market behavior appear in the laboratory
• We construct an experimental asset market with a monotonically decreasing fundamental value.
• All traders are videotaped throughout the session• The videotapes are analysed with Noldus Facereader at a later date.• The Facereader reads a facial expression and classifies it along seven
dimensions.– These correspond to the basic universal emotions identified by Ekman
(1978, 1999)
Facereading software• We use facereading software to track traders’ emotional state while the
market is operating.• These are
– Happiness– Anger– Sadness– Disgust– Fear– Surprise– Neutrality
• Facereader also reports the overall valence of emotion.• Unlike other neuroeconomic methodologies, Facereader translates
physiological responses directly into of psychological models
Initial valence and average prices
Valence is typically negative: Experiments are not fun for subjects
We hypothesize that:(1) More
positive valence is associated with higher prices.
r = .708, p < .01
Initial fear and average pricesWe hypothesize that:
(2) Fear is associated with lower prices
= -.549p < .06
People who are more neutral during crash period earn more money Crash period: The period with the largest price decrease
Hypothesis 4:
Neutrality during market turbulence is associated with greater earnings
Correlations Between Loss Aversion Measure and Emotions
Fear Valence Happiness Anger Surprise Disgust Sadness Neutral
Loss
aversion
0.3427***
(0.018)
-0.3012**
(0.025)
-0.0459
(0.759)
-0.0680
(0.649)
-0.0851
(0.569)
0.2098
(0.157)
0.1096
(0.463)
-0.1989
(0.180)
Correlations between initial emotional state and likelihood of being a FV trader
Initial Emotional State Fundamental Value Trader
Neutral .202**
Happy .012
Sad -.050
Angry -.195**
Surprised -.091
Scared -.207**
Disgusted -.182*
Valence .097
More positive valence predicts more purchases
Buy t Buy t Sell t Sell t
Model 1 Model 2 Model 3 Model 4
valence t-1 .237* .238* fear t-1 2.151 2.690
money t-1 7.29e-06 4.95e-06 money t-1 5.79e-06 6.25e-06
units t-1 -.021** -.015 units t-1 .050*** .046***
P level t-1 -.00007 -.00008 P level t-1 -.00012 -.00012
Buy t-1 .355*** Sell t-1 .362***
Prob>chi2
=0.000Prob>chi2 =0.0586
Prob>chi2 =0.000Prob> chi2
=0.0000
9770 obs
49 groups
9770 obs
49 groups
9971 obs
50 groups
9971 obs
50 groups
Momentum traders buy more when they are in a more positive emotional state
Buy t Fundamental Value Trader Momentum Trader Rational Speculator Trader
Valence t-1 .280 .521** -.025
Money t-1 .00003 -.00004 -.00007**
Units t-1 .015 -.119*** -.068***
Price level t-1 -.0004* .0007*** 3.15e-06
Buy t-1 .435*** .141* .370***
Obs: 3762
Groups: 19
Prob>F =.0000
Obs: 3396
Groups: 17
Prob>F =.0000
Obs: 2612
Groups: 13
Prob>F =.0000
More fear correlates with more sales
Buy t Buy t Sell t Sell t
Model 1 Model 2 Model 3 Model 4
valence t -.004 .003 fear t 4.995*** 4.861***
money t-1 9.00e-06 6.50e-06
money t-1 3.42e-06 3.87e-06
units t-1 -.020**-.015
units t-1 .048***.044***
P level t-1 -.00011-.00011
P level t-1 .00012-.00012
Buy t-1 .355*** Sell t-1 .363***
Prob>chi2=0.000 Prob>chi2 =.1950 Prob>chi2 =0.000 Prob>chi2 =0.000
9769 obs
49 groups
9769 obs
49 groups
9970 obs
50 groups
9970 obs
50 groups
More neutral traders are less active submitting offers
bids t asks t Bids&asks t
neutrality t-1 -.247** -.031 -.126*
money t-1 -.00008*** .00005*** 9.81e-06
units t-1 -.058*** .054*** .020***
price level t-1 -.0002 .0001* -.0001
bids t-1 .128***
asks t-1 .097***
Bids&askst-1 .103***
Obs: 9770
Groups: 49
Prob>F =.000
Obs: 9971
Groups: 50
Prob>F =.000
Obs: 9971
Groups: 50
Prob>F =.000
Better overall financial position improves traders’ emotional state
Valence t Valence t
money t-12.51e-06* 4.01e-06**
units t-1.0013* .0022***
P level t-1-.000044*** -.000087***
valence t-1.480***
const.-.028** -.046***
Obs: 9927
Groups: 50
Prob>F =.000
Obs: 9970
Groups: 50
Prob>F =.000
Profitable purchases lead to higher valence
Valence t
Model 1
Valence t
Model 2
Valence t
Model 3
const -.031*** -.016*** -.005*
buy t-1 .001 .001 .002
sell t-1 .004 .003 .002
valence t-1 .483*** .472***
(FV-Price)*buy t-1 .00002**
(Price-FV)*sell t-1 -.00001*
Obs: 9970
Groups: 50
Prob>F =.6734
R2=0.0001
Obs: 9927
Groups: 50
Prob>F =.000
R2=0.355
Obs: 8990
Groups: 49
Prob>F =.000
R2=0.351
Excessive prices associated with
negative valence
At the market level, fear increases the probability of prices decreasing, while the rest of the emotions appear to help sustain bubbles
Random Effects Fixed Effects
Fear 354.45** 98.08
Neutral -6.35** -14.27***
Happiness -6.14** -15.19***
Anger -5.40* -14.30***
Disgust -6.17 -20.25***
Sad -2.96 -10.68**
constant 6.46**
Conclusions • Bubbles and crashes in experimental markets are a complex phenomenon,
with many determinants of their magnitude.• Cash balances and quantity of shares available for sale influence price
levels.• Some time paths of fundamentals are more conducive to mispricing than
others.• The preference parameters of risk and loss aversion predict prices and
quantity traded. • Cognitive ability/motivation predicts mispricing. • There are consistent emotional substrates to market bubbles and crashes.• Emotional state of traders responds to and can predict subsequent market
activity.
Part 3: An experimental DSGE economy
• Construct an experimental macroeconomy, similar in structure to a New Keynesian DSGE macroeconomy. Close enough so that hypothesis from NK DSGE model can be evaluated.
• Three types of (infinitely lived) agents– Consumers: supply labor, purchase (3) products, and save for
the future– Producers: purchase labor, produce one of the (3) products, sell
output– Central bank: sets interest rates
• Preferences and productivity subject to shocks
Producer incentives
• Maximize profit:Пit = pityit – wtLit
yit = AtLit
At = A0 + γAt-1 + δεt Where Пit = profit of firm i in period t
pit = price of good i in period t
yit = production of good i in t
wt = wage in t
Lit = labor bought by i in t
At = productivity parameter in t
εt = productivity shock in t
γ = 0.8, δ = 0.2, A0 = 0.7
Consumer incentives• Payoff in period t of consumer j = βt[Ujt(Cjt) – Dj(Ljt)] • Ujt(Cjt)=∑ihijt[cijt
(1-σ)/(1- σ)]• hijt = μij + τhijt-1 + δεjt
• D(Ljt) = d*Ljt1+η/ (1+η)
• Where Cjt= consumption at time t of consumer jLjt = labor supplied at t Dj(Ljt) = disutility to j of labor he supplies at tcijt = consumption of good i by consumer j at t ε jt = preference shock for consumer j in period t
β = .99, μij = 120, τ = 0.8, d = 15, η = 2, n = 3.
Consumer incentives
• Faces a budget constraint: wtLjt + 1/n∑iΠi,t-1 + (1 + rt)sj,t-1 = ∑ipitcijt + sjt
• sjt can be thought of as savings or bonds• Create monopolistic competition with
different preference shocks for each good.
Experimental Design
• Timing within a period
• Stage 1: Labor market– There is a shock to productivity at the beginning of each
period.– A double auction market operates for labor.– Cost of supplying labor and productivity is (privately) known
at the time of trade.– Sales take place in terms of (fiat) experimental currency.
Costs of labor supply are incurred in terms of utility (Euros). • Production occurs automatically
– Each producer has available a quantity of his product to sell for stage 2
Stage 2 of a period:Product market
• There is a shock to consumer preferences.• Sellers post prices • Buyers purchase units of each of the three products
at their own pace– Product transactions take place in terms of (fiat)
experimental currency– Valuations are in terms of utility (Euro paid to the subjects)– It is possible that some units will go unsold, or that stock
will have been depleted at the time a consumer wants to buy.
Savings, producer profit, discounting, and ending the experiment
• Consumers’ unspent cash is saved for later periods, and earns interest.• Producers’ unspent cash (profit) is awarded to the consumers in equal
shares. – However, the agents acting as producers received a payment in Euro equal
proportionally to their profits. The payment was corrected for inflation. • The game goes at least 50 periods, randomly stopping between periods 50
- 70. • Utility (euro earnings) from consumption and labor supply exhibit a
decreasing trend of 1% per period.• The final cash balance of consumers is “bought out” by the experimenter.• Interest rate set by an instrumental rule:
rt = π* + 1.5(πt-1 - π*), π* = .03where, πt = inflation in period t, π* = inflation target
Timing of a session
• A session took 3 ¾ – 4 ¾ hours.• Instructions read (~30 minutes)• 5 period practice economy (~30 minutes)• > 50 period economy that counted toward
earnings.• Placed bounds on wages and prices for the
first two periods.
The treatments
• (1) Baseline– The conditions described above
• (2) Human Central Banker: In each period, three agents each chose an interest rate. The
group’s decision (and thus the rate in effect) was the median of the three choices.
The agents had an incentive to minimize the loss function Losst = (πt – π*)2
Central bankers were paid an amount equal to max{0, a – b*Loss}
• (3) Menu Cost:– To change the price from one period to the next,
producers had to pay a cost equal to: 0.025*pi,t-1*yit
– Otherwise identical to Baseline
• (4) Low Friction– Valuations are the same for each good (μij = μ0), though
differ by individual and by time period (εjt > 0). – Otherwise identical to Baseline– Parameters set to equate welfare to Baseline under a
simulation we conducted.
TreatmentsMonopolistic Competition
Human central banker
Menu cost for product price change (= .025[pi,t-1*yit])
Baseline Y N N
Menu cost Y N Y
Human central banker
Y Y N
Low friction N N N
Hypothesis
• Persistence of shocks (effect beyond the current period): – Treatment differences– In treatments Baseline, Human Central Banker, and Low Friction no
persistence, in treatment Menu Cost, shocks are persistent (both Menu Cost and market power are needed for persistence in New Keynesian DSGE model).
• Empirical stylized fact is that a shock to interest rates, output, or inflation, has persistent effects on itself and on some of the other two variables.
• Also can compare between treatments– GDP, inflation, welfare, employment, etc…
Results: GDP
GDP is highest under Low Friction
GDP is lowest in the late periods under Human Central Banker
Menu Costs do not affect GDP
Results: Inflation
Inflation rate is similar on average in all four treatments, including Human Central Bankers Volatility is lowest under Menu costsVolatility is highest under Human Central Banker
-20
020
40In
flatio
n ra
te
0 10 20 30 40 50ppp
HCB baselinemenu_cost low_friction
Inflation - across treatments
A degree of heterogeneity exists within each treatment
02
004
006
008
001
000
Rea
l GD
P
0 10 20 30 40 50ppp
session2 session3session11 session12
Real GDP - baseline treatment
Impulse Responses: Baseline treatment
Productivity shock persistent
Shock to GDP/Productivity
Shock to demand Inflation
Shock to Interest Rate
Row 1-3: Effect on output, inflation, interest rates
Inflation Shock Persistent
Interest Rate Shock persistent
Price Puzzle
Inflation shock has persistent effect on policy
Impulse Responses: Baseline treatment
Productivity shock persistent
Shock to GDP/Productivity
Shock to demand Inflation
Shock to Interest Rate
Row 1-3: Effect on output, inflation, interest rates
Inflation Shock Persistent
Interest Rate Shock persistent
Price Puzzle
Inflation shock has persistent effect on policy
Impulse Responses: Menu Cost treatment
Shock to GDP/Productivity
Shock to demand Inflation
Shock to Interest Rate
Row 1 - 3: Effect on output, inflation, interest rates
Productivity shock persistent
Interest Rate Shock persistent
Price Puzzle
Impulse Responses: Low frictionShock to GDP/Productivity
Shock to demand Inflation
Shock to Interest Rate
Row 1: Effect on output
Row 2: Effect on inflation
Row 3: Effect on interest rates
Productivity shock persistentProductivity shock lowers prices
Impulse Responses: Human Central Banker
Shock to GDP/Productivity
Shock to demand Inflation
Shock to Interest Rate
Row s1-3: Effect on output, inflation, interest rates
Productivity shock persistent
GDP responds positively to interest rate shock. Income effect Productivity
shock lowers prices
Interest Rate Shock persistent
Inflation shock has persistent effect on policy
Price Puzzle
Evaluating the hypothesis• Very little persistence in the Low Friction treatment.
• Somewhat more persistence in Menu Cost than in Baseline.
• More persistence of policy (interest rate) shocks in Human Central Banker treatment
• Mixed evidence with regard to hypothesis. Monopolistic competition alone generates shock persistence, and menu costs add to it.
How do the human central bankers set interest rates?
• Consider the following regression• rt = β0 + β1rt-1 + β2πt-1 + β3(GDP – GDP*)
• System GMM Dynamic Panel Estimator (Blundell and Bond, 1998)
• Taylor rule coefficient (relationship between interest rate and inflation) = β2/(1- β1) [Bullard and Mitra, 2007]
• Implied coefficient is close to 2, consistent with the Taylor principle since it is greater than 1.
Interest rate at t rt
Constant Interest rate at t-1, rt-1
Inflation at t-1, πt-1
Output gap GDP-GDP*
Estimate (std err)
.4212 (.2609)
.9395 (.0146)
.1538 (.0114)
.0184 (.0074)
Hazard Rate of Price Changes
Average duration of price spells similar except for Menu Cost, in which it is longer.
In Menu Cost, the hazard function is upward sloping. Price is more likely to change the longer it has been unchanged
Conclusions• Methodology
– It is feasible to construct a DSGE model in the laboratory. It is possible to verify stylized facts and consider the effect of some assumptions from a behavioral standpoint.
• Persistence– Monopolistic competition, in conjunction with multiple agents and
bounded rationality, is sufficient to generate some persistence– Menu costs add somewhat to this persistence.– Discretionary central banking affects shock persistence.– Negligible persistence in Low Friction. Biases in decision making of
producers and consumers alone do not generate persistence.