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INFLATION EXPECTATIONS AND MONETARY POLICY DESIGN: EVIDENCE FROM THE LABORATORY

Published online by Cambridge University Press:  05 December 2016

Damjan Pfajfar  and
Blaž Žakelj
Damjan Pfajfar*
Affiliation:
Board of Governors of the Federal Reserve System
Blaž Žakelj
Affiliation:
Universitat Pompeu Fabra
*
Address correspondence to: Damjan Pfajfar, Board of Governors of the Federal Reserve System, 20th Street and Constitution Avenue N.W., Washington, DC 20551, USA; e-mail: [email protected].
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Abstract

Using laboratory experiments within a New Keynesian framework, we explore the interaction between the formation of inflation expectations and monetary policy design. The central question in this paper is how to design monetary policy when expectations formation is not perfectly rational. Instrumental rules that use actual rather than forecasted inflation produce lower inflation variability and reduce expectational cycles. A forward-looking Taylor rule where a reaction coefficient equals 4 produces lower inflation variability than rules with reaction coefficients of 1.5 and 1.35. Inflation variability produced with the latter two rules is not significantly different. Moreover, the forecasting rules chosen by subjects appear to vary systematically with the policy regime, with destabilizing mechanisms chosen more often when inflation control is weaker.

Keywords

Laboratory ExperimentsInflation ExpectationsNew Keynesian ModelMonetary Policy Design
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Issue 4 , June 2018 , pp. 1035 - 1075
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

We would like to thank the associate editor, two anonymous referees, Klaus Adam, Steffan Ball, Bruno Biais, Wändi Bruine de Bruin, Tim Cogley, John Duffy, Chryssi Giannitsarou, Cars Hommes, Seppo Honkapohja, Tobias Klein, Aniol Llorente Saguer, Ramon Marimon, Rosemarie Nagel, Charles Noussair, Jan Potters, Juan Manuel Puerta, John Roberts, Emiliano Santoro, Mike Woodford, and other participants at the Bank of Canada, Bank of England, Humbolt University Berlin, University of Amsterdam, Universitat Pompeu Fabra, Erasmus University Rotterdam, European University Institute, University of Hamburg, Catholic University in Milan, Tilburg University, University of West Virginia, 2nd LICTEM Conference in Barcelona, 2010 New York FRB Conference on Consumer Inflation Expectations, 2008 Learning Week at St. Louis FED, 2008 Computational Economics Conference in Paris, 2008 ESA meetings in Lyon, and 2008 Nordic Conference on Behavioral and Experimental Economics in Copenhagen for their comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Federal Reserve Board.

References

REFERENCES

Adam, K. (2007) Experimental evidence on the persistence of output and inflation. Economic Journal 117, 603–636.CrossRefGoogle Scholar
Anufriev, M. and Hommes, C. (2012) Evolution of market heuristics. Knowledge Engineering Review 27, 255–271.Google Scholar
Arifovic, J. and Petersen, L. (2015) Escaping Expectations-Driven Liquidity Traps: Experimental Evidence. Discussion Papers dp15-03, Department of Economics, Simon Fraser University.Google Scholar
Arifovic, J. and Sargent, T.J. (2003) Laboratory experiments with an expectational Phillips curve. In Altig, D. E. and Smith, B. D. (eds.), Evolution and Procedures in Central Banking. Cambridge, UK: Cambridge University Press.Google Scholar
Assenza, T., Heemeijer, P., Hommes, C., and Massaro, D. (2013) Individual Expectations and Aggregate Macro Behavior. Tinbergen Institute Discussion Papers 13-016/II, Tinbergen Institute.CrossRefGoogle Scholar
Bao, T. and Duffy, J. (2013) Adaptive Vs. Eductive Learning: Theory and Evidence. Working Papers 518, Department of Economics, University of Pittsburgh.Google Scholar
Bao, T., Duffy, J. and Hommes, C. H. (2013) Learning, forecasting and optimizing: An experimental study. European Economic Review 61, 186–204.Google Scholar
Bao, T., Hommes, C. and Makarewicz, T. (2015) Bubble formation and (in)efficient markets in learning-to-forecast and -optimise experiments. Economic Journal. In press.Google Scholar
Bao, T., Hommes, C., Sonnemans, J., and Tuinstra, J. (2012) Individual expectations, limited rationality and aggregate outcomes. Journal of Economic Dynamics and Control 36, 1101–1120.CrossRefGoogle Scholar
Berardi, M. (2007) Heterogeneity and misspecifications in learning. Journal of Economic Dynamics and Control 31, 3203–3227.CrossRefGoogle Scholar
Bernanke, B. (2007) Inflation expectations and inflation forecasting. Technical report, Speech at National Bureau of Economic Research Summer Institute, Cambridge, Massachusetts.Google Scholar
Bernanke, B. S. and Woodford, M. (1997) Inflation forecasts and monetary policy. Journal of Money, Credit and Banking 29, 653–684.Google Scholar
Bernasconi, M. and Kirchkamp, O. (2000) Why do monetary policies matter? An experimental study of saving and inflation in an overlapping generations model. Journal of Monetary Economics 46, 315–343.Google Scholar
Blundell, R.W. and Bond, S.R. (1998) Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143.Google Scholar
Branch, W. A. (2004) The theory of rationally heterogeneous expectations: Evidence from survey data on inflation expectations. Economic Journal 114, 592–621.Google Scholar
Carroll, C. D. (2003) Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics 118, 269–298.Google Scholar
Conover, W. J. (1999) Practical Nonparameteric Statistics, 3rd ed. New York: Wiley.Google Scholar
Cooley, T. F. and Prescott, E. C. (1995) Economic growth and business cycles. In Cooley, T. F. (ed.), Frontiers of Business Cycle Research, pp. 1–120. Princeton, NJ: Princeton University Press.Google Scholar
Duffy, J. (2016) Macroeconomics: A survey of laboratory research. In Kagel, J. H. and Roth, A. E. (eds.), Handbook of Experimental Economics, vol. 2, pp. 1–90. Princeton, NJ: Princeton University Press.Google Scholar
Evans, G. W. and Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.Google Scholar
Evans, G. W., Honkapohja, S. and Marimon, R. (2001) Convergence in monetary inflation models with heterogeneous learning rules. Macroeconomic Dynamics 5, 1–31.CrossRefGoogle Scholar
Evans, G. W., Honkapohja, S. and Williams, N. (2010) Generalized stochastic gradient learning. International Economic Review 51, 237–262.Google Scholar
Fehr, E. and Tyran, J.-R. (2008) Limited rationality and strategic interaction: The impact of the strategic environment on nominal inertia. Econometrica 76, 353–394.Google Scholar
Fischbacher, U. (2007) z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics 10, 171–178.CrossRefGoogle Scholar
Friedman, M. (1948) A monetary and fiscal framework for economic stability. American Economic Review 38, 245–264.Google Scholar
Friedman, M. (1960) A Program for Monetary Stability. New York: Fordham University Press.Google Scholar
Hazelett, D. and Kernen, A. (2002) Hyperinflation and Seigniorage in an Experimental Overlapping Generations Economy. Mimeo, Whitman College.Google Scholar
Heemeijer, P., Hommes, C., Sonnemans, J., and Tuinstra, J. (2009) Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation. Journal of Economic Dynamics and Control 33, 1052–1072.Google Scholar
Hommes, C. (2011) The heterogeneous expectations hypothesis: Some evidence from the lab. Journal of Economic Dynamics and Control 35, 1–24.Google Scholar
Hommes, C., Massaro, D. and Weber, M. (2015) Monetary Policy Under Behavioral Expectations: Theory and Experiment. Tinbergen Institute Discussion Papers 15-087/II, Tinbergen Institute.Google Scholar
Hommes, C., Sonnemans, J., Tuinstra, J., and van de Velden, H. (2005) Coordination of expectations in asset pricing experiments. Review of Financial Studies 18, 955–980.Google Scholar
Hommes, C. and Zhu, M. (2014) Behavioral learning equilibria. Journal of Economic Theory 150, 778–814.Google Scholar
Ireland, P. N. (2004) Technology shocks in the new Keynesian model. Review of Economics and Statistics 86, 923–936.Google Scholar
Kelley, H. and Friedman, D. (2008) Learning to forecast rationally. In Plott, C. R. and Smith, V. L. (eds.), Handbook of Experimental Economics Results, vol. 1, chap. 35, pp. 303–310. Amsterdam: Elsevier.Google Scholar
Kryvtsov, O. and Petersen, L. (2013) Expectations and Monetary Policy: Experimental Evidence. Working Papers 13-44, Bank of Canada.Google Scholar
Marcet, A. and Nicolini, J. P. (2003) Recurrent hyperinflations and learning. American Economic Review 93, 1476–1498.Google Scholar
Marcet, A. and Sargent, T. J. (1989) Convergence of least-squares learning in environments with hidden state variables and private information. Journal of Political Economy 97, 1306–1322.Google Scholar
Marimon, R., Spear, S. E. and Sunder, S. (1993) Expectationally driven market volatility: An experimental study. Journal of Economic Theory 61, 74–103.Google Scholar
Marimon, R. and Sunder, S. (1993) Indeterminacy of equilibria in a hyperinflationary world: Experimental evidence. Econometrica 61, 1073–1107.Google Scholar
Marimon, R. and Sunder, S. (1994) Expectations and learning under alternative monetary regimes: An experimental approach. Economic Theory 4, 131–162.Google Scholar
Marimon, R. and Sunder, S. (1995) Does a constant money growth rule help stabilize inflation: Experimental evidence. Carnegie–Rochester Conference Series on Public Policy 45, 111–156.Google Scholar
McCallum, B. T. and Nelson, E. (1999) Performance of operational policy rules in an estimated semi-classical model. In Taylor, J. (ed.), Monetary Policy Rules, pp. 15–45. Chicago, IL: University of Chicago Press.Google Scholar
Noussair, C. N., Pfajfar, D. and Zsiros, J. (2011) Frictions, Persistence, and Central Bank Policy in an Experimental Dynamic Stochastic General Equilibrium Economy. Discussion Paper 2011-030, Center for Economic Research, Tilburg University.Google Scholar
Noussair, C.N., Pfajfar, D. and Zsiros, J. (2015) Pricing decisions in an experimental dynamic stochastic general equilibrium economy. Journal of Economic Behavior & Organization 109, 188–202.CrossRefGoogle Scholar
Petersen, L. (2015) Do expectations and decisions respond to monetary policy? Journal of Economic Studies 42, 972–1004.Google Scholar
Pfajfar, D. and Santoro, E. (2010) Heterogeneity, learning and information stickiness in inflation expectations. Journal of Economic Behavior & Organization 75, 426–444.Google Scholar
Pfajfar, D. and Žakelj, B. (2011) Uncertainty and Disagreement in Forecasting Inflation: Evidence from the Laboratory. Discussion Paper 2011-053, Center for Economic Research, Tilburg University.Google Scholar
Pfajfar, D. and Žakelj, B. (2014) Experimental evidence on inflation expectation formation. Journal of Economic Dynamics and Control 44, 147–168.Google Scholar
Taylor, J. B. (1993) Discretion versus policy rules in practice. Carnegie–Rochester Conference Series on Public Policy 39, 195–214.Google Scholar
Walsh, C. E. (2003) Monetary Theory and Policy, 2nd ed. Cambridge, MA: The MIT Press.Google Scholar
Williams, A. W. (1987) The formation of price forecasts in experimental markets. Journal of Money, Credit and Banking 19, 1–18.Google Scholar
Woodford, M. (2003) Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton, NJ: Princeton University Press.Google Scholar