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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
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Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

Web page: http://www.EconomicDynamics.org/
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