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Projections Parameterized Expectations Algorithms (Fortran)


  • Christian Haefke

    (University of California, San Diego)


These programs use the techniques described in Ken Judd's 1992 "Journal of Economic Theory" article to solve the standard growth model using parameterized expectations. Another good reference for the solution methods used in these programs is the working paper "Algorithms for Solving Dynamic Models with Occasionally Binding Constraints" by Larry Christiano and Jonas Fisher. All algorithms have the following properties. 1. They use the tensor method to approximate the conditional expectation with orthogonal Chebyshev polynomials. 2. The coefficients of the approximating function are such that they minimize the distance between the approximating function and the numerically calculated conditional expectation at a set of grid points. 3. The grid points are Chebyshev nodes. 4. The numerical integration procedure used to calculate the conditional expectation is Hermite Gaussian Quadrature. In my experience it is easier to obtain an accurate solution fast with quadrature methods than with Monte Carlo methods. 5. The "iterative" programs iterate on a projection procedure to find the coefficients of the approximating function. 6. The "equation-solver" programs use a nonlinear equation solver to find the value of the coefficients at which the approximating function equal the numerically calculated conditional expectation.

Suggested Citation

  • Christian Haefke, 1999. "Projections Parameterized Expectations Algorithms (Fortran)," QM&RBC Codes 67, Quantitative Macroeconomics & Real Business Cycles.
  • Handle: RePEc:dge:qmrbcd:67

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