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A Solution Method For Linear Rational Expectation Models Under Imperfect Information

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. John H. Boyd & Michael Dotsey, 1990. "Interest rate rules and nominal determinacy," Working Paper 90-01, Federal Reserve Bank of Richmond.
    6. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
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    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.

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    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

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