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Learning, adaptive expectations, and technology shocks

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  • Kevin X. D. Huang
  • Zheng Liu
  • Tao Zha

Abstract

This study explores the macroeconomic implications of adaptive expectations in a standard real business cycle model. When rational expectations are replaced by adaptive expectations, we show that the self-confirming equilibrium is the same as the steady state rational expectations equilibrium for all admissible parameters, but that dynamics around the steady state are substantially different between the two equilibria. The differences are driven mainly by the dampened wealth effect and the strengthened intertemporal substitution effect, not by the escapes emphasized by Williams (2003). As a result, adaptive expectations can be an important source of frictions that amplify and propagate technology shocks and seem promising for generating plausible labor market dynamics.

Suggested Citation

  • Kevin X. D. Huang & Zheng Liu & Tao Zha, 2008. "Learning, adaptive expectations, and technology shocks," Working Paper Series 2008-18, Federal Reserve Bank of San Francisco.
  • Handle: RePEc:fip:fedfwp:2008-18
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    Cited by:

    1. Kuang, Pei, 2014. "A model of housing and credit cycles with imperfect market knowledge," European Economic Review, Elsevier, vol. 70(C), pages 419-437.
    2. Gelain, Paolo & Lansing, Kevin J., 2014. "House prices, expectations, and time-varying fundamentals," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 3-25.
    3. Richard Evans & Kerk Phillips, 2014. "OLG Life Cycle Model Transition Paths: Alternate Model Forecast Method," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 105-131, January.
    4. Paolo Gelain & Kevin J. Lansing & Caterina Mendicino, 2013. "House Prices, Credit Growth, and Excess Volatility: Implications for Monetary and Macroprudential Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 9(2), pages 219-276, June.
    5. Berardi, Michele & Galimberti, Jaqueson K., 2017. "Empirical calibration of adaptive learning," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 219-237.
    6. Mitra, Kaushik & Evans, George W. & Honkapohja, Seppo, 2013. "Policy change and learning in the RBC model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 1947-1971.
    7. repec:eee:dyncon:v:83:y:2017:i:c:p:215-231 is not listed on IDEAS
    8. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    9. Colin Caines, 2016. "Can Learning Explain Boom-Bust Cycles In Asset Prices? An Application to the US Housing Boom," International Finance Discussion Papers 1181, Board of Governors of the Federal Reserve System (U.S.).
    10. Federico di Pace & Kaushik Mitra & Shoujian Zhang, 2014. "Adaptive Learning and Labour Market Dynamics," CDMA Working Paper Series 201408, Centre for Dynamic Macroeconomic Analysis.
    11. Berardi, Michele & Galimberti, Jaqueson K., 2017. "On the initialization of adaptive learning in macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 26-53.
    12. Orlando Gomes, 2009. "Stability under learning: the neo-classical growth problem," Economics Bulletin, AccessEcon, vol. 29(4), pages 3186-3193.
    13. William Branch & Bruce McGough, 2011. "Business cycle amplification with heterogeneous expectations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 47(2), pages 395-421, June.
    14. Paolo Gelain & Kevin J. Lansing & Gisle J. Natvik, 2015. "Explaining the Boom-Bust Cycle in the U.S. Housing Market: A Reverse-Engineering Approach," Working Paper 2015/11, Norges Bank.
    15. Hommes, Cars & Zhu, Mei, 2014. "Behavioral learning equilibria," Journal of Economic Theory, Elsevier, vol. 150(C), pages 778-814.
    16. Agnieszka Markiewicz, 2012. "Model Uncertainty And Exchange Rate Volatility," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 815-844, August.
    17. Suda, J., 2013. "Belief shocks and the macroeconomy," Working papers 434, Banque de France.
    18. Pei Kuang, 2013. "Imperfect Knowledge About Asset Prices and Credit Cycles," Discussion Papers 13-02r, Department of Economics, University of Birmingham.
    19. Chevillon, Guillaume & Massmann, Michael & Mavroeidis, Sophocles, 2010. "Inference in models with adaptive learning," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 341-351, April.
    20. Pei Kuang, 2013. "Imperfect Knowledge about Asset Prices and Credit Cycles," CDMA Working Paper Series 201303, Centre for Dynamic Macroeconomic Analysis.
    21. Doshchyn, Artur & Giommetti, Nicola, 2013. "Learning, Expectations, and Endogenous Business Cycles," MPRA Paper 49617, University Library of Munich, Germany.
    22. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.

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    Keywords

    Macroeconomics;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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