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NONLOCAL SOLUTIONS TO DYNAMIC EQUILIBRIUM MODELS: THE APPROXIMATE STABLE MANIFOLDS APPROACH

Published online by Cambridge University Press:  14 February 2018

Viktors Ajevskis*
Affiliation:
Bank of Latvia
*
Address correspondence to: Viktors Ajevskis, Bank of Latvia, Kr. Valdemara Street, 2A, Riga LV-1050, Latvia; e-mail: Viktors.Ajevskis@bank.lv.

Abstract

This study presents a method for constructing a sequence of approximate solutions of increasing accuracy to general equilibrium models on nonlocal domains. The method is based on a technique originated from dynamical systems theory. The approximate solutions are constructed employing the Contraction Mapping Theorem and the fact that the solutions to general equilibrium models converge to a steady state. Under certain nonlocal conditions, the convergence of the approximate solutions to the true solution is proved. We also show that the proposed approach can be treated as a rigorous proof of convergence for the extended path algorithm in a class of nonlinear rational expectation models.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

I would like to thank an associate editor and a referee for the thoughtful comments and suggestions. The views expressed in this paper are the sole responsibility of the author and do not necessarily reflect the position of the Bank of Latvia.

References

REFERENCES

Adjemian, Stéphane and Juillard, Michel (2011) Accuracy of the Extended Path Simulation Method in a New Keynesian Model with Zero Lower Bound on the Nominal Interest Rate. Mimeo, Université du Maine.Google Scholar
Barnett, William A. and He, Yijun (2006) Singularity bifurcations. Journal of Macroeconomics 28, 522Google Scholar
Barnett, William A. and Chen, Guo (2015) Bifurcation of Macroeconometric Models and Robustness of Dynamical Inferences. Studies in Applied Economics, No. 32, Johns Hopkins Institute for Applied Economics, Global Health, and Study of Business Enterprise.Google Scholar
Barnett, William A., Serletis, Apostolos, and Serletis, Demitre (2015) Nonlinear and complex dynamics in economics. Macroeconomic Dynamics 19, 17491779.Google Scholar
Blanchard, Olivier J. and Kahn, Charles M. (1980) The solution of linear difference models under rational expectations. Econometrica 48, 13051311.Google Scholar
Brock, William A. and Mirman, Leonard J. (1972) Optimal economic growth and uncertainty. the discounted case. Journal of Economic Theory 4, 479513.Google Scholar
Chow, Shui-Nee and Hale, Jack K. (1982) Methods of Bifurcation Theory. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Collard, Fabrice and Juillard, Michel (2001) Accuracy of stochastic perturbation methods: The case of asset pricing models. Journal of Economic Dynamics and Control 25, 979999.Google Scholar
Fair, Ray and Taylor, John (1983) Solution and maximum likelihood estimation of dynamic rational expectation models. Econometrica 51, 11691185.Google Scholar
Gagnon, Joseph E. (1990) Solving stochastic equilibrium models with the extended path method. Journal of Business and Economic Statistics 8, 3536.Google Scholar
Gagnon, Joseph E. and Taylor, John (1990) Solving the stochastic growth model by deterministic extended path. Economic Modelling 7, 251257.Google Scholar
Golub, Gene H. and Van Loan, Charles F. (1996) Matrix Computations, 3rd ed. Baltimore and London: Johns Hopkins University Press.Google Scholar
Hartmann, Philip (1982) Ordinary Differential Equations, 2nd ed. New York: Wiley.Google Scholar
Jin, He-Hui and Judd, Kenneth L. (2002) Perturbation Methods for General Dynamic Stochastic Models. Discussion paper, Hoover Institution, Stanford.Google Scholar
Judd, Kenneth L. (1998) Numerical Methods in Economics, Cambridge, MA: MIT Press.Google Scholar
Katok, Anatole and Hasselblatt, Boris (1995) Introduction to the Modern Theory of Dynamical Systems. Cambridge: Cambridge University Press.Google Scholar
King, Robert G. and Watson, Mark W. (2002) System reduction and solution algorithms for singular linear difference systems under rational expectations. Computational Economics 20, 5786.Google Scholar
Laffargue, Jean-Pierre (1990) Résolution d'un modèle macroéconomique avec anticipations rationnelles. Annales d'Economie et Statistique 17, 97119.Google Scholar
Love, David R. F. (2010) Revisiting deterministic extended-path: A simple and accurate solution method for macroeconomic models. International Journal of Computational Economics and Econometrics 1, 309316.Google Scholar
Lipton, David, Poterba, James, Sachs, Jeffrey, and Summers, Lawrence H. (1982) Multiple shooting in rational expectations models. Econometrica 50, 13291333.Google Scholar
Lyapunov, Aleksandr (1949) Probleme général de la stabilité du mouvement, Annals of Mathematics Studies, no 17. Princeton, NJ: Princeton University Press.Google Scholar
Maliar, Lilia, Maliar, Serguei, Taylor, John, and Tsener, Inna (2015) A Tractable Framework for Analyzing a Class of Nonstationary Markov Models. NBER working paper 21155.Google Scholar
Nitecki, Zbigniew (1971) Differentiable Dynamics. Cambridge, MA: MIT Press.Google Scholar
Potzsche, Christian (2010) Geometric Theory of Discrete Nonautonomous Dynamical Systems. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Schmitt-Grohé, Stephanie and Uribe, Martín (2004) Solving dynamic general equilibrium models using as second-order approximation to the policy function. Journal of Economic Dynamics and Control 28, 755775.Google Scholar
Smale, Steven (1967) Differentiable dynamical systems. Bulletin of the American Mathematical Society 73, 747817.Google Scholar
Stuart, Andrew (1990) Numerical analysis of dynamical systems. Acta Numerica 3, 467572.Google Scholar
Zeidler, Eberhard (1986) Nonlinear Functional Analysis and Its Applications: I. Berlin, Heidelberg: Springer-Verlag.Google Scholar