IDEAS home Printed from https://ideas.repec.org/a/cup/macdyn/v15y2011i04p465-494_99.html
   My bibliography  Save this article

A Solution Method For Linear Rational Expectation Models Under Imperfect Information

Author

Listed:
  • Shibayama, Katsuyuki

Abstract

This article presents a solution algorithm for linear rational expectation models under imperfect information, in which some decisions are made based on smaller information sets than others. In our solution representation, imperfect information does not affect the coefficients on crawling variables, which implies that, if a perfect-information model exhibits saddle-path stability, for example, the corresponding imperfect-information models also exhibit saddle-path stability. However, imperfect information can significantly alter the quantitative properties of a model. Indeed, this article demonstrates that, with a predetermined wage contract, the standard RBC model remarkably improves the correlation between labor productivity and output.

Suggested Citation

  • Shibayama, Katsuyuki, 2011. "A Solution Method For Linear Rational Expectation Models Under Imperfect Information," Macroeconomic Dynamics, Cambridge University Press, vol. 15(4), pages 465-494, September.
    • Handle: RePEc:cup:macdyn:v:15:y:2011:i:04:p:465-494_99
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1365100509990897/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. King, Robert G & Watson, Mark W, 1998. "The Solution of Singular Linear Difference Systems under Rational Expectations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1015-1026, November.
    2. 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.
    3. Boyd Iii, J.H. & Dotsey, M., 1990. "Interest Rate Rules And Nominal Determinacy," RCER Working Papers 222, University of Rochester - Center for Economic Research (RCER).
    4. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    5. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
    6. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Carravetta, Francesco & Sorge, Marco M., 2013. "Model reference adaptive expectations in Markov-switching economies," Economic Modelling, Elsevier, vol. 32(C), pages 551-559.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pengfei Wang & Yi Wen, 2006. "Solving linear difference systems with lagged expectations by a method of undetermined coefficients," Working Papers 2006-003, Federal Reserve Bank of St. Louis.
    2. Alali, Walid Y., 2009. "Solution Strategies of Dynamic Stochastic General Equilibrium (DSGE) models," EconStor Preprints 269876, ZBW - Leibniz Information Centre for Economics.
    3. Alali, Walid Y., 2009. "Solution Strategies of Dynamic Stochastic General Equilibrium (DSGE) models," MPRA Paper 116480, University Library of Munich, Germany.
    4. Richard Mash, 2003. "A Note on Simple MSV Solution Methods for Rational Expectations Models of Monetary Policy," Economics Series Working Papers 173, University of Oxford, Department of Economics.
    5. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    6. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    7. Fritz Breuss & Katrin Rabitsch, 2009. "An estimated two-country DSGE model of Austria and the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(1), pages 123-158, February.
    8. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.
    9. Dennis, Richard, 2004. "Solving for optimal simple rules in rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1635-1660, June.
    10. 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.
    11. Meyer-Gohde, Alexander, 2010. "Linear rational-expectations models with lagged expectations: A synthetic method," Journal of Economic Dynamics and Control, Elsevier, vol. 34(5), pages 984-1002, May.
    12. Onatski, Alexei, 2006. "Winding number criterion for existence and uniqueness of equilibrium in linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 323-345, February.
    13. Enrique Martínez García, 2016. "Finite-Order VAR Representation of Linear Rational Expectations Models: With Some Lessons for Monetary Policy," Globalization Institute Working Papers 285, Federal Reserve Bank of Dallas.
    14. Frank Hespeler, 2008. "Solution Algorithm to a Class of Monetary Rational Equilibrium Macromodels with Optimal Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 207-223, April.
    15. Jonathan J Adams, 2023. "Equilibrium Determinacy With Behavioral Expectations," Working Papers 001008, University of Florida, Department of Economics.
    16. Rondina, Giacomo & Walker, Todd B., 2021. "Confounding dynamics," Journal of Economic Theory, Elsevier, vol. 196(C).
    17. Baele, Lieven & Bekaert, Geert & Cho, Seonghoon & Inghelbrecht, Koen & Moreno, Antonio, 2015. "Macroeconomic regimes," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 51-71.
    18. Wang, Pengfei & Wen, Yi, 2007. "Inflation dynamics: A cross-country investigation," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2004-2031, October.
    19. Mariano Kulish & Adrian Pagan, 2017. "Estimation and Solution of Models with Expectations and Structural Changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 255-274, March.
    20. Luisa Corrado & Sean Holly, 2006. "The Linearisation and Optimal Control of Large Non-Linear Rational Expectations Models by Persistent Excitation," Computational Economics, Springer;Society for Computational Economics, vol. 28(2), pages 139-153, September.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:macdyn:v:15:y:2011:i:04:p:465-494_99. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/mdy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.