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Invertible and non-invertible information sets in linear rational expectations models

Author

Listed:
  • Brad Baxter

    (UCL - University College of London [London])

  • Liam Graham

    (Department of Economics - UCL - University College of London [London])

  • Stephen Wright

    (UCL - University College of London [London])

Abstract

Rational expectations solutions are usually derived by assuming that all state variables relevant to forward-looking behaviour are directly observable, or that they are "...an invertible function of observables" (Mehra and Prescott, 1980). Using a framework that nests linearised DSGE models, we give a number of results useful for the analysis of linear rational expectations models with restricted information sets. We distinguish between instantaneous and asymptotic invertibility, and show that the latter may require significantly less information than the former. We also show that non-invertibility of the information set can have significant implications for the time-series properties of economies.

Suggested Citation

  • Brad Baxter & Liam Graham & Stephen Wright, 2010. "Invertible and non-invertible information sets in linear rational expectations models," Post-Print hal-00767497, HAL.
  • Handle: RePEc:hal:journl:hal-00767497
    DOI: 10.1016/j.jedc.2010.11.002
    Note: View the original document on HAL open archive server: https://hal.science/hal-00767497
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Alexandre Kohlhas, 2015. "Learning-by-Sharing: Monetary Policy and the Information Content of Public Signals," 2015 Meeting Papers 57, Society for Economic Dynamics.
    2. Mario Forni & Luca Gambetti & Marco Lippi & Luca Sala, 2017. "Noisy News in Business Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 122-152, October.
    3. Manoj Atolia & Ryan Chahrour, 2020. "Intersectoral Linkages, Diverse Information, and Aggregate Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 36, pages 270-292, April.
    4. Sorge Marco M., 2020. "Computing sunspot solutions to rational expectations models with timing restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(2), pages 1-10, June.
    5. Donald Robertson & Stephen Wright, 2012. "The Predictive Space, or, If x predicts y, what does y tell us about x?," Birkbeck Working Papers in Economics and Finance 1210, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Thomas Lubik & Christian Matthes & Elmar Mertens, 2023. "Indeterminacy and Imperfect Information," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 37-57, July.
    7. Hauk, Esther & Lanteri, Andrea & Marcet, Albert, 2021. "Optimal policy with general signal extraction," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 54-86.
    8. Mario Forni & Luca Gambetti & Luca Sala, 2017. "News, Uncertainty and Economic Fluctuations (No News is Good News)," Center for Economic Research (RECent) 132, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    9. Manoj Atolia & Ryan Chahrour, 2020. "Intersectoral Linkages, Diverse Information, and Aggregate Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 36, pages 270-292, April.
    10. Anna Kormilitsina, 2013. "Solving Rational Expectations Models with Informational Subperiods: A Perturbation Approach," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 525-555, April.
    11. Thomas Lubik & Christian Matthes & Elmar Mertens, 2023. "Indeterminacy and Imperfect Information," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 37-57, July.
    12. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2019. "Information, VARs and DSGE Models," School of Economics Discussion Papers 1619, School of Economics, University of Surrey.
    13. Hürtgen, Patrick, 2014. "Consumer misperceptions, uncertain fundamentals, and the business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 279-292.
    14. Forni, Mario & Gambetti, Luca & Sala, Luca, 2017. "News, Uncertainty and Economic Fluctuations," CEPR Discussion Papers 12139, C.E.P.R. Discussion Papers.
    15. Andrea Lanteri & Albert Marcet & Esther Hauk, 2014. "Optimal Policy with Endogenous Signal Extraction," 2014 Meeting Papers 677, Society for Economic Dynamics.
    16. Seong-Hoon Kim, 2012. "Sequential Action and Beliefs Under Partially Observable DSGE Environments," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 219-244, October.
    17. Huo, Zhen & Pedroni, Marcelo, 2023. "Dynamic information aggregation: Learning from the past," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 107-124.
    18. Ryan Chahrour & Manoj Atolia, 2015. "Intersectoral Linkages, Diverse Information, and Aggregate Dynamics in a Neoclassical Model," 2015 Meeting Papers 398, Society for Economic Dynamics.
    19. Liam Graham, 2011. "Learning, information and heterogeneity," CDMA Working Paper Series 201113, Centre for Dynamic Macroeconomic Analysis.
    20. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2023. "Imperfect Information and Hidden Dynamics," School of Economics Discussion Papers 1223, School of Economics, University of Surrey.

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    More about this item

    Keywords

    D80; E32; Imperfect information; Invertibility; Rational expectations; Fundamental versus nonfundamental time series representations; Kalman filter; Dynamic stochastic general equilibrium;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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