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Computationally efficient solution and maximum likelihood estimation of nonlinear rational expectation models

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  • Hoyt Bleakley
  • Jeffrey C. Fuhrer

Abstract

This paper presents new, computationally efficient algorithms for solution and estimation of nonlinear dynamic rational expectations models. The innovations in the algorithms are as follows: (1) The entire solution path is obtained simultaneously by taking a small number of Newton steps, using analytic derivatives, over the entire path; (2) The terminal conditions for the solution path are derived from the uniqueness and stability conditions from the linearization of the model around the terminus of the solution path; (3) Unit roots are allowed in the model; (4) Very general models with expectational identities and singularities of the type handled by the King-Watson (1995a,b) linear algorithms are also allowed; and (5) Rank-deficient covariance matrices that arise owing to the presence of expectational identities are admissible. Reasonably complex models are solved in less than a second on a Sun Sparc20. This speed improvement makes derivative-based estimation methods feasible. Algorithms for maximum likelihood estimation and sample estimation problems are presented.

Suggested Citation

  • Hoyt Bleakley & Jeffrey C. Fuhrer, 1996. "Computationally efficient solution and maximum likelihood estimation of nonlinear rational expectation models," Working Papers 96-2, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:96-2
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    Cited by:

    1. Giovanni Olivei & Silvana Tenreyro, 2007. "The Timing of Monetary Policy Shocks," American Economic Review, American Economic Association, vol. 97(3), pages 636-663, June.
    2. Anthony Garratt & Kevin Lee & Mohammad Hashem Pesaran & Yongcheol Shin, 1998. "A structural cointegrating VAR approach to macroeconometric modelling," Edinburgh School of Economics Discussion Paper Series 8, Edinburgh School of Economics, University of Edinburgh.
    3. Fuhrer, Jeffrey C., 1997. "Towards a compact, empirically-verified rational expectations model for monetary policy analysis," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 197-230, December.
    4. Paolo Zagaglia, 2005. "Solving Rational-Expectations Models through the Anderson-Moore Algorithm: An Introduction to the Matlab Implementation," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 91-106, August.
    5. Sung Ho Park, 2013. "Estimating Quarterly Different Price and Wage Rigidity and Its Implication for Monetary Policy," 2013 Meeting Papers 1367, Society for Economic Dynamics.

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