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Parameter Learning in General Equilibrium: The Asset Pricing Implications

Author

Listed:
  • Pierre Collin-Dufresne
  • Michael Johannes
  • Lars A. Lochstoer

Abstract

Parameter learning strongly amplifies the impact of macroeconomic shocks on marginal utility when the representative agent has a preference for early resolution of uncertainty. This occurs as rational belief updating generates subjective long-run consumption risks. We consider general equilibrium models with unknown parameters governing either long-run economic growth, rare events, or model selection. Overall, parameter learning generates long-lasting, quantitatively significant additional macroeconomic risks that help explain standard asset pricing puzzles. (JEL C52, D83, E13, E32, G12)

Suggested Citation

  • Pierre Collin-Dufresne & Michael Johannes & Lars A. Lochstoer, 2016. "Parameter Learning in General Equilibrium: The Asset Pricing Implications," American Economic Review, American Economic Association, vol. 106(3), pages 664-698, March.
  • Handle: RePEc:aea:aecrev:v:106:y:2016:i:3:p:664-98
    Note: DOI: 10.1257/aer.20130392
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    References listed on IDEAS

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    Cited by:

    1. Tortorice, Daniel L., 2018. "Equity return predictability, time varying volatility and learning about the permanence of shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 315-343.
    2. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2015. "The Tail that Wags the Economy: Beliefs and Persistent Stagnation," NBER Working Papers 21719, National Bureau of Economic Research, Inc.
    3. Koulovatianos, Christos & Li, Jian & Weber, Fabienne, 2018. "Market fragility and the paradox of the recent stock-bond dissonance," Economics Letters, Elsevier, vol. 162(C), pages 162-166.
    4. Anisha Ghosh & George M. Constantinides, 2017. "What Information Drives Asset Prices?," NBER Working Papers 23689, National Bureau of Economic Research, Inc.
    5. Winkler, Fabian, 2016. "The Role of Learning for Asset Prices and Business Cycles," Finance and Economics Discussion Series 2016-019, Board of Governors of the Federal Reserve System (US), revised 01 Mar 2017.
    6. repec:nbr:nberch:14074 is not listed on IDEAS
    7. Klaus Adam & Albert Marcet & Johannes Beutel, 2017. "Stock Price Booms and Expected Capital Gains," American Economic Review, American Economic Association, vol. 107(8), pages 2352-2408, August.
    8. Ivan Rudik & Derek Lemoine & Maxwell Rosenthal, 2018. "General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy," 2018 Meeting Papers 369, Society for Economic Dynamics.
    9. Gollier, Christian, 2016. "Evaluation of long-dated assets: The role of parameter uncertainty," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 66-83.
    10. Bansal, Ravi & Kiku, Dana & Yaron, Amir, 2016. "Risks for the long run: Estimation with time aggregation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 52-69.
    11. Anagol, Santosh & Balasubramaniam, Vimal & Ramadorai, Tarun, 2018. "Learning from Noise: Evidence from India's IPO Lotteries," CEPR Discussion Papers 13314, C.E.P.R. Discussion Papers.
    12. repec:eee:jfinec:v:128:y:2018:i:3:p:504-534 is not listed on IDEAS

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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