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Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models

  • Serguei Maliar

    (Hoover Institution at Stanford University and University of Alicante)

  • Lilia Maliar

    (Hoover Institution at Stanford University and University of Alicante)

  • Kenneth Judd

    (Hoover Institution at Stanford University)

We develop numerically stable stochastic simulation approaches for solving dynamic economic models. We rely on standard simulation procedures to simultaneously compute an ergodic distribution of state variables, its support and the associated decision rules. We differ from existing methods, however, in how we use simulation data to approximate decision rules. Instead of the usual least-squares methods, we examine a variety of alternatives, including the least-squares method using SVD, Tikhonov regularization, least-absolute deviation methods, principal components regression method, all of which are numerically stable and can handle ill-conditioned problems. These new methods enable us to compute high-order polynomial approximations without encountering numerical problems. Our approaches are especially well suitable for high-dimensional applications in which other methods are infeasible.

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Paper provided by Society for Economic Dynamics in its series 2010 Meeting Papers with number 280.

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Date of creation: 2010
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Handle: RePEc:red:sed010:280
Contact details of provider: Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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Web page: http://www.EconomicDynamics.org/society.htm
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  1. Aiyagari, S Rao, 1994. "Uninsured Idiosyncratic Risk and Aggregate Saving," The Quarterly Journal of Economics, MIT Press, vol. 109(3), pages 659-84, August.
  2. Lilia Maliar & Serguei Maliar, 2002. "The Representative Consumer In The Neoclassical Growth Model With Idiosyncratic Shocks," Working Papers. Serie AD 2002-20, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
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  9. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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  12. Lilia Maliar & Serguei Maliar, 2001. "Parametrized Expectations Algorithm And The Moving Bounds," Working Papers. Serie AD 2001-23, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  13. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
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  15. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, June.
  16. Den Haan, Wouter J & Marcet, Albert, 1994. "Accuracy in Simulations," Review of Economic Studies, Wiley Blackwell, vol. 61(1), pages 3-17, January.
  17. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  18. Albert Marcet & Guido Lorenzoni, 1998. "The Parameterized Expectations Approach: Some Practical Issues," QM&RBC Codes 128, Quantitative Macroeconomics & Real Business Cycles.
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