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Parameterized expectations approach; Some practical issues


  • Albert Marcet
  • Guido Lorenzoni


We discuss some practical issues related to the use of the Parameterized Expectations Approach (PEA) for solving non-linear stochastic dynamic models with rational expectations. This approach has been applied in models of macroeconomics, financial economics, economic growth, contract theory, etc. It turns out to be a convenient algorithm, especially when there is a large number of state variables and stochastic shocks in the conditional expectations. We discuss some practical issues having to do with the application of the algorithm, and we discuss a Fortran program for implementing the algorithm that is available through the internet. We discuss these issues in a battery of six examples.

Suggested Citation

  • Albert Marcet & Guido Lorenzoni, 1998. "Parameterized expectations approach; Some practical issues," Economics Working Papers 296, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:296

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    References listed on IDEAS

    1. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
    2. Coleman, Wilbur John, II, 1991. "Equilibrium in a Production Economy with an Income Tax," Econometrica, Econometric Society, vol. 59(4), pages 1091-1104, July.
    3. Domowitz, Ian & El-Gamal, Mahmoud A., 1993. "A Consistent Test of Stationary-Ergodicity," Econometric Theory, Cambridge University Press, vol. 9(04), pages 589-601, August.
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    Cited by:

    1. Budria, Santiago & Díaz, Antonia, 2006. "Term premium and equity premium in economies with habit formation," UC3M Working papers. Economics we065522, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Alexandre Dmitriev, 2006. "Technological Transfers, Limited Commitment and Growth," Computing in Economics and Finance 2006 248, Society for Computational Economics.
    3. Gianluca Femminis, 2007. "From simple growth to numerical simulations: a primer in dynamic programming," DISCE - Quaderni dell'Istituto di Teoria Economica e Metodi Quantitativi itemq0745, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    4. Michael Reiter, 2015. "Solving OLG Models with Asset Choice," 2015 Meeting Papers 1509, Society for Economic Dynamics.
    5. Carceles Poveda, Eva, 2003. "Capital adjustment costs and firm risk aversion," Economics Letters, Elsevier, vol. 81(1), pages 101-107, October.
    6. Tsvetanka Karagyozova, 2007. "Asset Pricing with Heterogeneous Agents, Incomplete Markets and Trading Constraints," Working papers 2007-46, University of Connecticut, Department of Economics, revised Sep 2008.
    7. Maldonado, Wilfredo L. & Moreira, Humberto Luiz Ataíde, 2006. "Solving Euler Equations: Classical Methods and the C^1 Contraction Mapping Method Revisited," Revista Brasileira de Economia - RBE, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 60(2), November.
    8. Yongsung Chang & Sun-Bin Kim, 2004. "Heterogeneity and Aggregation in the Labor Market: Implications for Aggregate Preference Shifts," Macroeconomics 0402024, EconWPA.
    9. repec:ebl:ecbull:v:3:y:2003:i:1:p:1-14 is not listed on IDEAS
    10. Reiter, Michael, 2015. "Solving OLG Models with Many Cohorts, Asset Choice and Large Shocks," Economics Series 320, Institute for Advanced Studies.
    11. Frank Portier & Luis A. Puch, "undated". "It's a Small Small Welfare Cost of Fluctuations," Working Papers 2005-26, FEDEA.
    12. Santiago Budría & Antonia Díaz, 2006. "Term and Equity Premium in Economies with Habit Formation," Working Papers 2006-23, FEDEA.

    More about this item


    Numerical algorithm; rational expectations; stochastic difference equations; simulation;

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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