RUHR ECONOMIC PAPERS Andreas Orland Michael W.M. Roos The New Keynesian Phillips Curve with Myopic Agents #281
Imprint Ruhr Economic Papers Published by Ruhr-Universität Bochum (RUB), Department of Economics Universitätsstr. 150, 44801 Bochum, Germany Technische Universität Dortmund, Department of Economic and Social Sciences Vogelpothsweg 87, 44227 Dortmund, Germany Universität Duisburg-Essen, Department of Economics Universitätsstr. 12, 45117 Essen, Germany Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI) Hohenzollernstr. 1-3, 45128 Essen, Germany Editors Prof. Dr. Thomas K. Bauer RUB, Department of Economics, Empirical Economics Phone: +49 (0) 234/3 22 83 41, e-mail: thomas.bauer@rub.de Prof. Dr. Wolfgang Leininger Technische Universität Dortmund, Department of Economic and Social Sciences Economics Microeconomics Phone: +49 (0) 231/7 55-3297, email: W.Leininger@wiso.uni-dortmund.de Prof. Dr. Volker Clausen University of Duisburg-Essen, Department of Economics International Economics Phone: +49 (0) 201/1 83-3655, e-mail: vclausen@vwl.uni-due.de Prof. Dr. Christoph M. Schmidt RWI, Phone: +49 (0) 201/81 49-227, e-mail: christoph.schmidt@rwi-essen.de Editorial Offi ce Joachim Schmidt RWI, Phone: +49 (0) 201/81 49-292, e-mail: joachim.schmidt@rwi-essen.de Ruhr Economic Papers #281 Responsible Editor: Thomas K. Bauer All rights reserved. Bochum, Dortmund, Duisburg, Essen, Germany, 2011 ISSN 1864-4872 (online) ISBN 978-3-86788-326-9 The working papers published in the Series constitute work in progress circulated to stimulate discussion and critical comments. Views expressed represent exclusively the authors own opinions and do not necessarily reflect those of the editors.
Ruhr Economic Papers #281 Andreas Orland and Michael W.M. Roos The New Keynesian Phillips Curve with Myopic Agents
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Andreas Orland and Michael W.M. Roos 1 The New Keynesian Phillips Curve with Myopic Agents Abstract Empirical estimations of the New Keynesian Phillips curve support hybrid versions with a positive weight on lagged inflation and a weight less than one on expected inflation. We argue that myopic price setting of some agents explains the low weight on expected inflation. The lagged term can be explained by trend extrapolation if information about the future is costly. In a laboratory experiment we implement the Calvo (1983) microfoundations of the Phillips curve. Both of our hypotheses are supported by the experimental data. About half of the subjects set optimal Calvo prices while about a third is myopic. JEL Classification: C91, D92, E52 Keywords: Hybrid Phillips curve; experimental economics; myopia; behavioral macroeconomicsl September 2011 1 Both RUB. All correspondence to Andreas Orland, Ruhr Universität Bochum (RUB), Universitätsstr. 150, 44801 Essen, Germany. E-Mail: andreas.orland@rub.de.
why
future p*t p t
1. z t*
weightingscheme weights >. =1
1. Can subjects average pricesetting behavior be described by the Calvo model?. 2. Istheremyopicbehavior? 3. Dosubjectsusepastinformation?
t, tj..
Table1:Summaryoftreatments =0.3 =0.3=0.7
Figure1:Timeseriesofdesiredpriceswithandwithouttrend 50 100 150 200 250 0 20 40 60 80 100 Period Desired prices (p*) of the No Trend treatments Desired prices (p*) of the Trend treatments =0.3=0.7). =0.7
Figure2:Theoreticalweightsimpliedbytheprobabilitiesused 0.2.4.6.8 0 1 2 3 4 5 6 Period ahead of t Weights for =.70 Weights for =.30 t
q x qx q
Figure3:Optimal,desiredandsetpricesinthebenchmarktreatment 70 80 90 100 110 120 130 0 20 40 60 80 100 Period Desired prices (p*) Optimal prices (z*) Set prices (z)
i
Table2:Estimatedweightsofthedesiredpriceswith=0.3 (1) j 460 502.459.354.472 496.497 278 261.238.075 053 018.031.019 italicsbold (1) j
Table3:Estimatedweightsofthedesiredpriceswith=0.7 (1) j 774 780 159 002 italicsbold (1) j =0.3 =0.7 =0.3.
Figure4:Optimal,desiredandsetpricesintreatment3 70 100 130 160 190 220 0 20 40 60 80 100 Period Desired prices (p*) Optimal prices (z*) Set prices (z)
n i n i =0.3
Table4:Meanratiosofacquiredfuturepricestosetprices Allsubjects Median Ignorantsubjects Informedsubjects =0.3 =0.3 =0.3 =0.7 =0.3 n i n i
=0.7 =0.3
Table5:Estimatedweightsofthedesiredpricesofinformedandignorantsubjects intreatments2and4 Treatment2 Treatment4 (1) j.432.296.021.489.217.625.008 italicsbold (1) j
Table6:Estimatedweightsofthedesiredpricesofinformedandignorantsubjects intreatments5and6 Treatment5 Treatment6 (1) j.322.072 =0.3 =0.7 =0.3.682.089.012 (1) j.851.104.199.636.084.034 italicsbold (1) j
j=0
3.
Table7:Biasandbackwardlookingbehavior =0.3 =0.3 =0.7 =0.3 =0.3 =0.3 =0.3 =0.7 =0.3.12.03.12.02.05.06.45.49.77.50.77.70.87.98.69 italicsbold 30
. =0.3 Table9:Estimatedstickinessparametersofalltreatmentswith=0.3
0= 1=1 0> 1<1 0=1 1=0 Table10:Classificationofsubjectsinalltreatments >
FigureA1:ScreenshotofthezTreeprogram FigureA2:ScreenshotofthezTreeprogramwithinformationcost
you you values Aslongasyouarenotallowedtosetanew value,youhavetosticktoyourpreviousvalue.probabilityp p cannotxq pqcannot qx optimalvalueifyouwereabletoreset yourvalueineachperiod. loss earning disproportionally p
current showupfee additional
TableC1:Observationspertreatment