Publication: A comparison and evaluation of some alternative solution methods to dynamics stochastic models
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1998
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Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
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We compare and evaluate the performance of four widely used numerical solution methods to dynamic rational expectations stochastic models, in the context of optimal and nonoptimal Pareto settings using a wide variety of statistical measures and two sample sizes. We find that: (i.) differences between methods do not necessarily increase with the complexity of the solved model (ii.) For all the example model economies we considered, a log-linear approximation behaves as well as a more complex to implernent finite element method. (iii.) Rejection of a particular solution method attending to the fulfilment of the rational expectation hypothesis is compatible with almost no differences between methods attending to other comparison criteria. (iv.) It is proper to consider 'large' sample sizes to check the properties of a particular solution.
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[1] Barañano, I., A. Iza and J. Vázquez (1997), "A comparison between the parameterized expectations and the log-linear simulation methods", Universidad del País Vasco.
[2] Binder, M. and H. M. Pesaran (1997), "Multivariate Rational Expectatioos Models and Macroeconomic Modeling: A Review and Some New Results" , forthcoming in Handbook of Applied Econometrics.
[3] Bona, J. L. and M. S. Santos (1997), "On the Role of Computation in Economic Theory", Journal of Economic Theory 72, 241-281.
[4] Brock, W. A. and L. Mirman (1972), "Optimal Economic Growth and Uncertainty: the Discounted Case", Journal of Economic Theory, 4, 479-513.
[5] Burnside, C. (1996), "Notes on Linearlzation and Discrete State Space Methods", Lecture Notes prepared for the 7th EEA Summer School, Florence, September.
[6] Campbell, J. (1994), "Inspecting the mechanism: an analytical approach to the stochastic growth model", Journal of Monetary Economics 33, pp. 463-506.
[7] Chow, G. C. (1997), Dynamic Economics, Oxford University Press.
[8] Christiano, L. and J. Fisher (1997), "Algorithms for Solving Dynamic Models with Occasionally Binding Constraints" , Northwestern University.
[9] Chriatiano, L. (1990) "Solving the Stochastic Growth Model by Linear-Quadratic Approximation and by Value-Function Iteration", Journal of Bussiness and Economic Statistics, vol. 8, 1.
[10] Christiano, L. (1990), "Linear-Quadratic Approximation and Value-Function Iteration: A Comparison", Journal of Business and Economic Statistics, vol. 8, 1.
[11] Cogley, T. and J. M. Nason (1993), "Impulse Dynamics and Propagation Mechanisms in a Real Business Cycle Model", Economics Letters 43, 77-81.
[12] Cooley, T. F. and G. Hansen (19a9), "The infLation tax in a Real Business Cycle Model", American Economic Review 4, vol. 79, 733-748.
[13] Cooley, T. F. and E. C. Prescott (1995), "Economic Growth and Business Cycles". Chapter 1 in T. F. Cooley (Ed.) Frontiers of Business Cycle Research, Princeton.
[14] Danthine J. P. and J. Donaldson (1995), "Computing Equilibria of Nonoptimal Economies", Chapter 3 in T. F. Cooley (Ed), Frontiers of Business Cycle Research, Princeton.
[15] Den Haan, W. and A. Marcet (1994), "Accuracy in simulations", Review of Economic Studies, 61, 3-17.
[16] Den Haan, W. and A. Marcet (1990), "Solving the Stochastic Growth Model by Parameterized Expectations", Journal of Business and Economic Statistics, vol. 8, 1.
[17] Díaz-Giménez, J. (1996), "Some very sketchy notes on the linear quadratic approximation to the Neoclassical Growth Model", Lecture Notes prepared for the 7th EEA Summer School, Florence, September.
[18] Domínguez, E. (1995), "Características de la estructura intertemporal de rentabilidades en un modelo de equilibrio general estocástico", Unpublished Ph. D., Universidad Complutense de Madrid.
[19] Hairault, J. O. (1995), "Presentation and Evaluation of the RBC Approach", in P. Y. Herun (Ed.) Advances in Bussiness Cycle Research. With Application to the French and US Economies, Springer-Verlag.
[20] Hansen, G. (1997), "Technica1 progress and aggregate fluctuations", Journal of Economic Dynamics and Control 21, 1005-1023.
[21] Hansen, G. (1985), "Indivisible labor and the business cycle", Journal of Monetary Economics 16, 309-327.
[22] Hansen, G. and E. C. Presoott (1995), "Recursive Methods for Computing Equilibria of Business Cycle Models", chapter 2 in T. F. Cooley (Ed.) Frontiers of Business Cycle Research, Princeton.
[23] Judd, K. (1996), "Approximation, perturbation and projection methods in economic analysis", Chapter 12 in Amann, H., D. Kendrick and J. Rust (Eds.) Handbook of Computational Economics, Amsterdam, Elsevier.
[24] Judd, K. L. (1997), "Computational Economics and Economic Theory: Substitutes or Complementa?", Journal of Economic Dynamics and Control, 21, 907-942.
[25] King, R., J. Plosser and S. Rebelo (1987), "Production, Growth and business cycles: technical appendix", Working Paper, University of Rochester.
[26] Kydland, F.E. (1989), "The Role of Money in a Competitive Model of Business Fluctuations", Carneige-Mellon University.
[27] Kydland, F.E. and E.C. Prescott (1996), "The Computational Experiment: An Econometric Tool" ,Journal of Economic Perspectives, 10, pp. 69-85.
[28] Lucas, R. (1980), "Methods and Problems in Business Cycle Theory", Journal of Money Credit and Banking, 12, pp. 696-715.
[29] Marcet, A. (1993), "Simulation Analysis of Dynamic Stochastic Models", chapter 3 in C. A. Sims (Ed.) Advances in Econometrics, Cambridge University Press.
[30] Marcet, A. and D. A. Marshall (1994), "Solving Nonlinear Rational Expectations Models by Parameterized Expectations: Convergence to Stationary Solutions", Institute for Empirical Macroeconomics, Discussion Paper 91, May.
[31] McGrattan, E. (1996), "Solving the Stochastic Growth Model with a Finite Element Method". Journal of Economic, Dynamics and Control 20, 19-42.
[32] McGrattan. E. (1996), "Lectures- EUI 1996", Lectures Notes prepared for the 7th EEA Summer School Florence, September.
[33] McGrattan, E. (1990), "Solving the Stochastic Growth Model by Linear-Quadratic Approximation", Journal of Buiness and Economic Statistics, vol. 8, 1.
[34] Novales, A. (1990), "Solving nonlinear rational expectations models: A stochastic equilibrium model of interest rates", Econometrica 58, pp. 93-111.
[35] Prescott, E. C. (1986), "Theory Ahead of Business Cycle Measurement", Federal Reserve Bank of Minneapolis Quarleriy Review 10, 9-22.
[36] Santos, M. (1997), "Accuracy based upon the Euler Equation residuals", mimeo.
[37] Sims, C. A. (1996), "Solving Linear Rational Expectations Models", Yale University.
[38] Sims, C. A. (1990), "Solving the Stochastic Growth Model by Backsolving with a Particular Nonlinear Decision Rule", Journal of Business and Economic Statistics, vol. 8, 1.
[39] Sims, C. A. (1989), "Solving Nonlinear Stochastic Optimization and Equilibrium Problems Backwards", Institute for Empirical Macroeconomics, Discussion Paper 15, May.
[40] TayJor, J. B. and H. Uhlig (1990), "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods", Journal of Business and Economic Statistics, vol. 8, l.
[41] Uhlig, H. (1997), "A toolkit for Analyzing Nonlinear Dynamic Stochastic Models Easily", Center for Economic Research, Tilburg University.
[42] Uhlig, H. (1996), "Linearization of Standard Optimization Based Business Cycle Models", Lecture Notes prepared for the 7th EEA Summer School, Florence.
[43] Vallés, J. (1997), "Aggregate investment in a business cycle model with adjustment costs", Journal of Economic Dynamics and Control 21, 1181-1198.