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Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching

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  • Tae-Seok Jang
  • Stephen Sacht

Abstract

In this paper we empirically examine a hybrid New-Keynesian model with heterogeneous bounded rational agents who may adopt an optimistic or pessimistic attitude - so called animal spirits - towards future movements of the output and inflation gap. The model is estimated via the simulated method of moments using Euro Area data from 1975Q1 to 2009Q4. In addition, we compare its empirical performance to the standard model with rational expectations. Our empirical results show that the model-generated auto- and cross-covariances of the output gap, the inflation gap and the nominal interest gap can provide a good approximation of the empirical second moments. The result is mainly driven by a high degree of persistence in the output and inflation gap due to the impact of animal spirits on economic activity. Furthermore, over the whole time interval the agents had expected moderate deviations of the future output gap from its steady state value.
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  • Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    • Handle: RePEc:bla:metroe:v:67:y:2016:i:1:p:76-113
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    1. Frank Smets & Raf Wouters, 2005. "Comparing shocks and frictions in US and euro area business cycles: a Bayesian DSGE Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 161-183.
    2. Goldbaum, David & Mizrach, Bruce, 2008. "Estimating the intensity of choice in a dynamic mutual fund allocation decision," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3866-3876, December.
    3. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2000. "Sticky Price Models of the Business Cycle: Can the Contract Multiplier Solve the Persistence Problem?," Econometrica, Econometric Society, vol. 68(5), pages 1151-1180, September.
    4. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2015. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 126-154.
    5. Guido Ascari & Tiziano Ropele, 2009. "Trend Inflation, Taylor Principle, and Indeterminacy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(8), pages 1557-1584, December.
    6. Russell, Bill & Banerjee, Anindya, 2008. "The long-run Phillips curve and non-stationary inflation," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1792-1815, December.
    7. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 40278, University Library of Munich, Germany.
    8. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
    9. Lee, Bong-Soo & Ingram, Beth Fisher, 1991. "Simulation estimation of time-series models," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 197-205, February.
    10. Duffie, Darrell & Singleton, Kenneth J, 1993. "Simulated Moments Estimation of Markov Models of Asset Prices," Econometrica, Econometric Society, vol. 61(4), pages 929-952, July.
    11. Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(2), pages 373-401, March.
    12. Magnus Forsells & Geoff Kenny, 2004. "Survey Expectations, Rationality and the Dynamics of Euro Area Inflation," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 13-41.
    13. Michael ARTIS & Massimiliano MARCELLINO & Tommaso PROIETTI, 2002. "Dating the Euro Area Business Cycle," Economics Working Papers ECO2002/24, European University Institute.
    14. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    15. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of animal spirits in a bounded rationality model: An application to the euro area," Kiel Working Papers 1798, Kiel Institute for the World Economy (IfW Kiel).
    16. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    17. Gaunersdorfer, Andrea & Hommes, Cars H. & Wagener, Florian O.O., 2008. "Bifurcation routes to volatility clustering under evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 27-47, July.
    18. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    19. Adriana Cornea-Madeira & Cars Hommes & Domenico Massaro, 2019. "Behavioral Heterogeneity in U.S. Inflation Dynamics," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 288-300, April.
    20. Boswijk, H. Peter & Hommes, Cars H. & Manzan, Sebastiano, 2007. "Behavioral heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1938-1970, June.
    21. Lombardi, Marco J. & Nicoletti, Giulio, 2012. "Bayesian prior elicitation in DSGE models: Macro- vs micropriors," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 294-313.
    22. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    23. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    24. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    25. David N. DeJong & Chetan Dave, 2007. "Introduction to Structural Macroeconometrics," Introductory Chapters, in: Structural Macroeconometrics, Princeton University Press.
    26. Michael W. M. Roos & Ulrich Schmidt, 2012. "The Importance of Time‐Series Extrapolation for Macroeconomic Expectations," German Economic Review, Verein für Socialpolitik, vol. 13(2), pages 196-210, May.
    27. Assenmacher-Wesche, Katrin & Gerlach, Stefan, 2008. "Interpreting euro area inflation at high and low frequencies," European Economic Review, Elsevier, vol. 52(6), pages 964-986, August.
    28. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    29. Matthias Lengnick & Hans-Werner Wohltmann, 2013. "Agent-based financial markets and New Keynesian macroeconomics: a synthesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 1-32, April.
    30. Andrea Gaunersdorfer & Cars Hommes, 2007. "A Nonlinear Structural Model for Volatility Clustering," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 265-288, Springer.
    31. George A. Akerlof, 2009. "How Human Psychology Drives the Economy and Why It Matters," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(5), pages 1175-1175.
    32. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    33. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    34. Steffen Ahrens & Stephen Sacht, 2014. "Estimating a high-frequency New-Keynesian Phillips curve," Empirical Economics, Springer, vol. 46(2), pages 607-628, March.
    35. Branch, William A. & McGough, Bruce, 2009. "A New Keynesian model with heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1036-1051, May.
    36. Luca Benati, 2008. "Investigating Inflation Persistence Across Monetary Regimes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(3), pages 1005-1060.
    37. Binder,M. & Pesaran,H.M., 1995. "Multivariate Rational Expectations Models and Macroeconomic Modelling: A Review and Some New Results," Cambridge Working Papers in Economics 9415, Faculty of Economics, University of Cambridge.
    38. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    39. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    40. Chiarella, Carl & He, Xue-Zhong, 2002. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 95-132, February.
    41. Jeffrey C. Fuhrer, 2006. "Intrinsic and Inherited Inflation Persistence," International Journal of Central Banking, International Journal of Central Banking, vol. 2(3), September.
    42. Paul Grauwe, 2011. "Animal spirits and monetary policy," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 47(2), pages 423-457, June.
    43. Castelnuovo, Efrem, 2010. "Trend inflation and macroeconomic volatilities in the post-WWII U.S. economy," The North American Journal of Economics and Finance, Elsevier, vol. 21(1), pages 19-33, March.
    44. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    45. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    46. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    47. Luca Benati & Paolo Surico, 2009. "VAR Analysis and the Great Moderation," American Economic Review, American Economic Association, vol. 99(4), pages 1636-1652, September.
    48. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, September.
    49. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian model: A formal test of backward- and forward-looking behavior," Economics Working Papers 2012-07, Christian-Albrechts-University of Kiel, Department of Economics.
    50. Moons, Cindy & Garretsen, Harry & van Aarle, Bas & Fornero, Jorge, 2007. "Monetary policy in the New-Keynesian model: An application to the Euro Area," Journal of Policy Modeling, Elsevier, vol. 29(6), pages 879-902.
    51. Leitemo, Kai, 2008. "Inflation-targeting rules: History-dependent or forward-looking?," Economics Letters, Elsevier, vol. 100(2), pages 267-270, August.
    52. Colin Camerer, 1998. "Bounded Rationality in Individual Decision Making," Experimental Economics, Springer;Economic Science Association, vol. 1(2), pages 163-183, September.
    53. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    54. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    55. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    56. Fagan, Gabriel & Henry, Jérôme & Mestre, Ricardo, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 42, European Central Bank.
    57. Franke Reiner, 2012. "Microfounded Animal Spirits in the New Macroeconomic Consensus," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-41, October.
    58. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
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    Cited by:

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    4. Jang, Tae-Seok & Sacht, Stephen, 2017. "Modeling consumer confidence and its role for expectation formation: A horse race," Economics Working Papers 2017-04, Christian-Albrechts-University of Kiel, Department of Economics.
    5. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    6. Tae-Seok Jang & Stephen Sacht, 2022. "Macroeconomic dynamics under bounded rationality: on the impact of consumers’ forecast heuristics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(3), pages 849-873, July.
    7. Christian Schoder, 2017. "Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood," Review of Keynesian Economics, Edward Elgar Publishing, vol. 5(4), pages 586–630-5, October.
    8. Franke, Reiner, 2022. "An empirical test of a fundamental Harrod-Kaldor business cycle model," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 1-14.
    9. Liang, Hanchao & Yang, Chunpeng & Cai, Chuangqun, 2017. "Beauty contest, bounded rationality, and sentiment pricing dynamics," Economic Modelling, Elsevier, vol. 60(C), pages 71-80.
    10. Cars Hommes & Robert Calvert Jump & Paul Levine, 2017. "Internal rationalityuyuyuy, heterogeneity and complexity in the New Keynesian model," Working Papers 20171706, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    11. Özge Dilaver & Robert Calvert Jump & Paul Levine, 2018. "Agent‐Based Macroeconomics And Dynamic Stochastic General Equilibrium Models: Where Do We Go From Here?," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1134-1159, September.
    12. Liang, Hanchao & Yang, Chunpeng & Zhang, Rengui & Cai, Chuangqun, 2017. "Bounded rationality, anchoring-and-adjustment sentiment, and asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 85-102.
    13. Kukacka, Jiri & Jang, Tae-Seok & Sacht, Stephen, 2018. "On the estimation of behavioral macroeconomic models via simulated maximum likelihood," Economics Working Papers 2018-11, Christian-Albrechts-University of Kiel, Department of Economics.
    14. Jump, Robert Calvert & Levine, Paul, 2019. "Behavioural New Keynesian models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 59-77.
    15. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    16. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    17. Jang Tae-Seok, 2020. "Animal spirits in an open economy: an interaction-based approach to the business cycle," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(1), pages 1-16, January.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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