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A Toolbox for the Study of Linear Difference Rational Expectations Models

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  • Oviedo, P. Marcelo

Abstract

By simplifying the computational tasks and by providing step-by-step explanations of the procedures required to study a linear dynamic rational expectations (LDRE) model, this paper and the accompanying "LDRE Toolbox" of Matalb functions guide a researcher with almost no experience in computational work to resolve and study his own model. After coding the model following specific guidelines, a single function call is all that is needed to log-linearize the model; simulate it under exogenous sequences of shocks; compute sample and population moment conditions; and obtain impulse-response functions. Three classical models in the Real-Business-Cycles literature are solved and studied throughout to give detailed examples of the steps involved in solving and studying LDRE models using the LDRE Toolbox. Namely, the economies in Brock and Mirman (Optimal Growth and Uncertainty: the Discounted Case, Journal of Economic Theory, 4(3): 479-513; 1972); King, Plosser, and Rebelo (Production, Growth and Business Cycles I: The Basic Neoclassical Model, Journal of Monetary Economics, 21: 195-232; 1988); and Mendoza (Real Business Cycles in a Small Open Economy, American Economic Review, 81(4): 797-818; 1991).

Suggested Citation

  • Oviedo, P. Marcelo, 2009. "A Toolbox for the Study of Linear Difference Rational Expectations Models," Staff General Research Papers Archive 12925, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12925
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    Cited by:

    1. Oviedo, P. Marcelo, 2005. "World Interest Rate, Business Cycles, and Financial Intermediation in Small Open Economies," Staff General Research Papers Archive 12360, Iowa State University, Department of Economics.

    More about this item

    Keywords

    Numerical methods; business cycles; linear difference; log linearization;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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