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The Modeling of Expectations in Empirical DSGE Models: a Survey


  • Fabio Milani

    () (Department of Economics, University of California-Irvine)


A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods. A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations. I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

Suggested Citation

  • Fabio Milani, 2012. "The Modeling of Expectations in Empirical DSGE Models: a Survey," Working Papers 121301, University of California-Irvine, Department of Economics.
  • Handle: RePEc:irv:wpaper:121301

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    References listed on IDEAS

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    Cited by:

    1. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    2. 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.
    3. Sergey Ivashchenko & Rangan Gupta, 2017. "Near-Rational Expectations: How Far are Surveys from Rationality?," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 60(1), pages 1-27.
    4. Agustín Arias, 2016. "Sentiment Shocks as Drivers of Business Cycles," Working Papers Central Bank of Chile 782, Central Bank of Chile.
    5. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    6. repec:eee:eecrev:v:100:y:2017:i:c:p:293-317 is not listed on IDEAS
    7. Kamber, Gunes & McDonald, Chris & Sander, Nick & Theodoridis, Konstantinos, 2016. "Modelling the business cycle of a small open economy: The Reserve Bank of New Zealand's DSGE model," Economic Modelling, Elsevier, vol. 59(C), pages 546-569.
    8. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    9. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    10. Hatcher, Michael & Minford, Patrick, 2014. "Stabilization policy, rational expectations and price-level versus infl‡ation targeting: a survey," CEPR Discussion Papers 9820, C.E.P.R. Discussion Papers.
    11. Güneş Kamber & Chris McDonald & Nicholas Sander & Konstantinos Theodoridis, 2015. "A structural model for policy analysis and forecasting: NZSIM," Reserve Bank of New Zealand Discussion Paper Series DP2015/05, Reserve Bank of New Zealand.
    12. Ahmed Jamal Pirzada, 2017. "Price Stickiness and Intermediate Materials Prices," Bristol Economics Discussion Papers 17/686, Department of Economics, University of Bristol, UK.
    13. Sergey Ivashchenko, 2014. "Near-Rational Expectations: How Far Are Surveys from Rationality?," EUSP Department of Economics Working Paper Series Ec-06/14, European University at St. Petersburg, Department of Economics.
    14. Hanck, Christoph & Prüser, Jan, 2016. "House prices and interest rates: Bayesian evidence from Germany," Ruhr Economic Papers 620, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Jochen Michaelis, 2013. "Und dann werfen wir den Computer an – Anmerkungen zur Methodik der DSGE-Modelle," MAGKS Papers on Economics 201323, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

    More about this item


    Expectations formation; DSGE models; Rational expectations; Adaptive learning; Survey expectations; New Bayesian macroeconometrics;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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