monetary policy under behavioral expectations: theory and...
TRANSCRIPT
Monetary Policy under Behavioral Expectations:Theory and Experiment
Matthias Weber
Bank of Lithuania & Vilnius University(joint work with Cars Hommes and Domenico Massaro)
Presentation at the European Central Bank, DG/R
January 23, 2018
Disclaimer: The views expressed are those of the authors and do not necessarily reflect those of the Bank of Lithuania.
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 1 / 33
Outline
Outline
1 Introduction
2 TheoryMacroeconomic ModelBehavioral Model of Expectation FormationEconomic Behavior and Policy Implications
3 ExperimentDesign and ImplementationTreatments and HypothesesResults
4 Discussion
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 2 / 33
Introduction
Introduction
Expectations play a crucial role in modern macroeconomic models
The standard assumption is that expectations are formed rationally
However, a lot of evidence of boundedly rational and irrationalbehavior in economics
What happens to the models and their conclusions if rationalexpectations are replaced by a behavioral model of expectationformation?
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 3 / 33
Introduction
Introduction
Behavioral expectations benchmark: heuristic switching model (fromearlier work)We compare results on aggregate economic behavior
Focus on inflation volatility (where the models yield different results)Inflation volatility / price stability of crucial importance to central banks
We derive testable hypotheses from the models with rational andbehavioral expectations and test them in a learning to forecastexperiment
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 4 / 33
Introduction
Introduction
Looking at it from the applied side (and narrowing down the researchquestion):
How is inflation volatility affected if the central bank reacts to theoutput gap with its interest rate decisions (in addition to reacting toinflation)?
Should a central bank that only cares about inflation (e.g. ECB) onlyreact to inflation or also to the output gap?
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 5 / 33
Introduction
Introduction
These questions can be investigated theoretically but alsoempirically/experimentallyEmpirical work with observational field data has some advantages,experimental work has others
No issues with reverse causality or confounding factorsWe know that the macro equations we use are actually the onesdetermining the outcomes in the experimental economy
In our experiment, we solely vary the feedback mechanism fromexpectations to realizations (by varying one parameter of the TaylorRule)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 6 / 33
Theory
1 Introduction
2 TheoryMacroeconomic ModelBehavioral Model of Expectation FormationEconomic Behavior and Policy Implications
3 ExperimentDesign and ImplementationTreatments and HypothesesResults
4 Discussion
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 7 / 33
Theory Macroeconomic Model
Macroeconomic Model
The aggregate equations are those of a standard New Keynesianclosed economy
These equations are also fully microfounded under behavioralexpectations (see Appendix A of the paper)
I will only show aggregate equations in this talk
Standard calibration for parameters (Clarida, Galí & Gertler, 2000)Calibration
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 8 / 33
Theory Macroeconomic Model
Macroeconomic Model
Aggregate New Keynesian Equations:
IS: yt = y et+1 − ϕ(it − πe
t+1) + gt
NKP: πt = λyt + ρπet+1 + ut
MP: it = max(π + φπ(πt − π) + φy (yt − y), 0)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 9 / 33
Theory Behavioral Model of Expectation Formation
Expectation Formation
As (benchmark) behavioral expectation formation mechanism, weconsider a heuristic switching model (HSM) that performed well inearlier work
Important: The results are extremely robust to using differentspecifications of behavioral expectation formation
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 10 / 33
Theory Behavioral Model of Expectation Formation
Expectation Formation
Beauty of the HSM:
Agents do not need to know the exact equations governing theeconomy
No high demands on agents’ computational abilities
Yet, agents are not “stupid”: They update the way they formexpectations over time (reinforcement learning)
Agents do not use heuristics much that performed poorly in the past
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 11 / 33
Theory Behavioral Model of Expectation Formation
Heuristics
Two ingredients, heuristics and switching mechanism
Individuals use the following four heuristic (2 period ahead forecasts;x either inflation or output gap):
ADA : x e1,t+1 = 0.65xt−1 + 0.35x e
1,t
WTR : x e2,t+1 = xt−1 + 0.4(xt−1 − xt−2)
STR : x e3,t+1 = xt−1 + 1.3(xt−1 − xt−2)
LAA : x e4,t+1 =
xavt−1 + xt−1
2 + (xt−1 − xt−2)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 12 / 33
Theory Behavioral Model of Expectation Formation
Switching between Heuristics
Agents choose between heuristics on the basis of past performance
Uh,t−1 = 1001 + |xt−1 − x e
h,t−1|+ ηUh,t−2
Updatingnh,t = δnh,t−1 + (1− δ) exp(βUh,t−1)∑
h exp(βUh,t−1)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 13 / 33
Theory Economic Behavior and Policy Implications
Price Stability
We care about price stability only
This is the mandate of the ECB (and the sole objective of some othercentral banks)
Which measure of price (in)stability / inflation volatility?
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 14 / 33
Theory Economic Behavior and Policy Implications
Measuring Inflation Volatility
Important: The results are qualitatively the same for all measures
Possibilities:
Mean squared deviation from target: 1T
∑Tt=1 (πt − π)2
Standard deviation:√
1T
∑Tt=1 (πt − πav )2
Relative deviation: 1T−1
∑Tt=2 (πt − πt−1)2
Precise welfare measure 1T
∑Tt=1 f (πt ,Vari (πe
i ,t))
We use the relative deviationExample
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 15 / 33
Theory Economic Behavior and Policy Implications
Main Theoretical Result
0.0 0.5 1.0 1.5
0.00
00.
010
0.02
0
phi_y
Infla
tion
vola
tility
(a) Rational model
0.0 0.2 0.4 0.6 0.8 1.0 1.2
0.00
0.04
0.08
phi_y
Infla
tion
vola
tility
(b) Behavioral model
Figure: Inflation volatility as function of φy
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 16 / 33
Theory Economic Behavior and Policy Implications
Policy Implications and Intuition
Policy implications of the behavioral model are straightforward:A CB that only cares about price stability should still react to theoutput gap!
What’s the intuition of the results?
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 17 / 33
Experiment
1 Introduction
2 TheoryMacroeconomic ModelBehavioral Model of Expectation FormationEconomic Behavior and Policy Implications
3 ExperimentDesign and ImplementationTreatments and HypothesesResults
4 Discussion
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 18 / 33
Experiment Design and Implementation
Introduction
Behavioral theory gives different results from rational theory
But how do actual people behave?One way to find out: experimentation
Full control:Macro equations are correct description of reality (by design)Incentivized elicitation of forecastsRandom assignment to treatments (no reverse causality, noconfounding factors, etc.)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 19 / 33
Experiment Design and Implementation
Introduction
The experiment is a learning-to-forecast experiment
We do not try to mimic all elements of the macroeconomy in thelaboratory with all possible choices!
We only elicit forecasts from subjects – everything else is done by thecomputerReflects the focus on expectation formation
The monetary policy rule changes the feedback from expectations torealizations
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 20 / 33
Experiment Design and Implementation
Design and Implementation
Subjects forecast output gap and inflation
Average forecast of a group is used as average expectation in themacro model to generate the next realization
Groups of 6
Inflation target is 3.5
Between subjects design & within session randomization
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 21 / 33
Experiment Design and Implementation
Design and Implementation
Subjects in both treatments receive absolutely identical instructions
Subjects receive only qualitative information about the experimentaleconomy
Each subject is either paid for inflation forecasts or output gapforecasts
Payment
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 22 / 33
Experiment Design and Implementation
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 23 / 33
Experiment Treatments and Hypotheses
Treatments
Two treatments, only difference is in the Taylor rule
T1: φπ = 1.5, φy = 0
T2: φπ = 1.5, φy = 0.5
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 24 / 33
Experiment Treatments and Hypotheses
Hypotheses
Outcome of interest is inflation volatility
Null-hypothesis derived from RE, alternative from BE:
T1 (φy = 0) T2 (φy = 0.5)
RE
BE
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 25 / 33
Experiment Results
Inflation Data
0 10 20 30 40 50
12
34
56
7
Inflation in T1
Period
Infla
tion
0 10 20 30 40 50
12
34
56
7
Inflation in T2
Period
Infla
tion
Figure: Realized inflation for all groups in both treatments
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 26 / 33
Experiment Results
Inflation Volatility
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.4
0.8
Inflation Volatility
EC
DF
T1T2
Figure: Empirical distribution functions of inflation volatility
Difference statistically significant (Wilcoxon rank-sum, p<0.01)Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 27 / 33
Experiment Results
Further Data: Output Gap
0 10 20 30 40 50
−3
−2
−1
01
23
4
Output Gap in T1
Period
Out
put G
ap
0 10 20 30 40 50
−3
−2
−1
01
23
4
Output Gap in T2
Period
Out
put G
ap
Figure: Realized output gap in both treatments
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 28 / 33
Experiment Results
Further Data: Interest Rates
0 10 20 30 40 50
02
46
810
Interest Rate in T1
Period
Inte
rest
rat
e
0 10 20 30 40 50
02
46
810
Interest Rate in T2
Period
Inte
rest
rat
e
Figure: Interest rate in both treatments
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 29 / 33
Experiment Results
Performance of HSM and other Models
Mean squared errors of two-period-ahead predictions from different modelsof expectation formation
Inflation T1 Output gap T1 Inflation T2 Output gap T2HSM 0.072 0.141 0.040 0.022RE 0.541 0.753 0.422 0.222ADA 0.254 0.399 0.168 0.095WTR 0.106 0.193 0.063 0.037STR 0.246 0.415 0.088 0.068LAA 0.107 0.180 0.063 0.037
Fractions
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 30 / 33
Discussion
1 Introduction
2 TheoryMacroeconomic ModelBehavioral Model of Expectation FormationEconomic Behavior and Policy Implications
3 ExperimentDesign and ImplementationTreatments and HypothesesResults
4 Discussion
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 31 / 33
Discussion
Discussion
We consider a macro model with behavioral expectations: results arepartly very different from the fully rational model
The behavioral model gives a policy recommendation that is differentfrom the same model with RE: Even a CB only interested in pricestability should react to changes in the output gap!
Laboratory evidence supports this policy recommendation
The evidence from the laboratory furthermore gives support to usingthe behavioral model
Additional intuition
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 32 / 33
Discussion
Thank you for your attention!
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Related Literature
Theory:Orphanides and Williams (2006), Branch and McGough (2009, 2010),Woodford (2010), De Grauwe (2011, 2012a), Anufriev et al. (2013),Kurz et al. (2013), etc.; see Woodford (2013) for an overview.
Experiments:Kryvtsov and Petersen (2013), Pfajfar and Zakelj (2014), Assenzaet al. (2014b), Cornand and M’Baye (2016), etc.; see Assenza et al.(2014a), and Cornand and Heinemann (2014) for an overview.
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Feedback
Is there additional intuition for our results?
From behavioral micro/finance: price expectations tend to deviateparticularly from fundamentals with sizable positive feedback (fromexpectations to realizations)
Matrix form of the macro equations (ZLB not binding)[
ytπt
]= Ω
[ϕπ(φπ − 1) + ϕφy yλϕπ(φπ − 1) + λϕφy y
]+ Ω
[1 ϕ(1− φπρ)λ λϕ+ ρ+ ρϕφy
] [y e
t+1πe
t+1
]+ Ω
[1 −ϕφπλ 1 + ϕφy
] [gtut
]
with Ω ≡ 1/(1 + λϕφπ + ϕφy )
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Feedback
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Feedback (+) Exp. Output Gap on Output Gap
phi_y
Val
ue
0.0 0.2 0.4 0.6 0.8 1.0
0.0
−0.
2−
0.4
−0.
6−
0.8
−1.
0
Feedback (−) Exp. Inflation on Output Gap
phi_y
Val
ue
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Feedback (+) Exp. Output Gap on Inflation
phi_y
Val
ue
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Feedback (+) Exp. Inflation on Inflation
phi_y
Val
ue
Finish
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Measuring Volatility: Example
0 10 20 30 40 50
02
46
8
Period
Infla
tion
Return
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Parameters
Parameters for the NK equations (in quarterly terms; Clarida, Galí,Gertler 2000)
ϕ = 1λ = 0.3ρ = 0.99
Return
Parameters for the heuristic switching model:δ = 0.9η = 0.7β = 0.4
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Incentives Subject Payments
0 2 4 6 8 100
10
20
30
40
50
60
70
80
90
100
absolute value forecast error
scor
e
Return
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Fractions Heuristics
10 20 30 40 50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
T1
Period
Frac
tion
heur
istic
s in
flatio
n
ADAWTRSTRLAA
10 20 30 40 50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
T2
Period
Frac
tion
heur
istic
s in
flatio
n
ADAWTRSTRLAA
10 20 30 40 50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
T1
Period
Frac
tion
heur
istic
s ou
tput
gap
ADAWTRSTRLAA
10 20 30 40 50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
T2
Period
Frac
tion
heur
istic
s ou
tput
gap
ADAWTRSTRLAA
Back
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
NK Model with Heterogeneous Expectations
NK model consistent with heterogeneous expectations in more generalform:
yt = y et+1 − ϕ(it − πe
t+1) + Φt(c) + gt
πt = λyt + ρπet+1 + Ψt(p) + ut
with
Φt(c) =∫
i(Ei ,tci ,t+1 − Ei ,tct+1)
Ψt(p) = (1− ω)β∫
i(Ei ,tpi ,t+1 − Ei ,tpt+1)
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
Random Utility Model
Agents i observe performance of each rule h with some noise
Uh = Uh + εhi
Ph = Pr [Uh > Uh′∀h′ 6=h] = Pr [Uh + εhi > Uh′ + εh′i∀h′ 6=h]
When error terms are IID following double exponential
Ph = exp(βUh)/∑
hexp(βUh)
β inversely proportional to noise varianceβ →∞: no errorsβ → 0: uniform probabilities
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33
Supplementary Material
References I
Anufriev, M., Assenza, T., Hommes, C. H., and Massaro, D. (2013).Interest rate rules and macroeconomic stability under heterogeneousexpectations. Macroeconomic Dynamics, 17:1574–1604.
Assenza, T., Bao, T., Hommes, C., and Massaro, D. (2014a). Experimentson expectations in macroeconomics and finance. In Duffy, J., editor,Experiments in Macroeconomics, volume 17 of Research in ExperimentalEconomics.
Assenza, T., Heemeijer, P., Hommes, C., and Massaro, D. (2014b).Managing self-organization of expectations through monetary policy: amacro experiment. CeNDEF Working Paper 14-07, University ofAmsterdam.
Benhabib, J., Evans, G. W., and Honkapohja, S. (2014). Liquidity trapsand expectation dynamics: Fiscal stimulus or fiscal austerity? Journal ofEconomic Dynamics and Control, 45:220–238.Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 30 / 33
Supplementary Material
References II
Branch, W. and McGough, B. (2009). A new keynesian model withheterogeneous expectations. Journal of Economic Dynamics andControl, 33:1036–1051.
Branch, W. and McGough, B. (2010). Dynamic predictors selection in anew keynesian model with heterogeneous expectations. Journal ofEconomic Dynamics and Control, 34(8):1492–1508.
Cornand, C. and Heinemann, F. (2014). Experiments on monetary policyand central banking. In Duffy, J., editor, Experiments inMacroeconomics, volume 17 of Research in Experimental Economics,pages 167–227.
Cornand, C. and M’Baye, C. K. (2016). Does inflation targeting matter?An experimental investigation. Macroeconomic Dynamics (forthcoming).
De Grauwe, P. (2011). Animal spirits and monetary policy. Economictheory, 47(2-3):423–457.
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 31 / 33
Supplementary Material
References III
De Grauwe, P. (2012a). Booms and busts in economic activity: Abehavioral explanation. Journal of Economic Behavior & Organization,83(3):484–501.
De Grauwe, P. (2012b). Lectures on behavioral macroeconomics.Princeton University Press, New Jersey.
De Grauwe, P. and Kaltwasser, P. R. (2012). Animal spirits in the foreignexchange market. Journal of Economic Dynamics and Control,36(8):1176–1192.
Duffy, J. (2012). Macroeconomics: A survey of laboratory research.Working Paper 334, University of Pittsburgh.
Evans, G. W. and Honkapohja, S. (2001). Learning and Expectations inMacroeconomics. Princeton University Press.
Kryvtsov, O. and Petersen, L. (2013). Expectations and monetary policy:Experimental evidence. Working Papers 13-44, Bank of Canada.
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 32 / 33
Supplementary Material
References IV
Kurz, M., Piccillo, G., and Wu, H. (2013). Modeling diverse expectationsin an aggregated new keynesian model. Journal of Economic Dynamicsand Control, 37:1403–1433.
Orphanides, A. and Williams, J. C. (2006). Monetary policy withimperfect knowledge. Journal of the European Economic Association,4(2-3):366–375.
Pfajfar, D. and Zakelj, B. (2014). Experimental evidence on inflationexpectations formation. Journal of Economic Dynamics and Control,44:147–168.
Woodford, M. (2010). Robustly optimal monetary policy with near-rationalexpectations. American Economic Review, 100(1):274–303.
Woodford, M. (2013). Macroeconomic Analysis Without the RationalExpectations Hypothesis. Annual Review of Economics, 5(1):303–346.
Hommes, Massaro, Weber Monetary Policy & Behavioral Expectations January 23, 2018 33 / 33