Advertisement

Computational Economics

, Volume 11, Issue 1–2, pp 71–87 | Cite as

Numerical Strategies for Solving the Nonlinear Rational Expectations Commodity Market Model

  • Mario J. Miranda
Article

Abstract

In this paper, I compare the accuracy, efficiency and stability of different numerical strategies for computing approximate solutions to the nonlinear rational expectations commodity market model. I find that polynomial and spline function collocation methods are superior to the space discretization, linearization and least squares curve-fitting methods that have been preferred by economists in the past.

nonlinear rational expectations models numerical methods 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Atkinson, K.E. (1989). An Introduction to Numerical Analysis, 2nd Ed. John Wiley & Sons, New York.Google Scholar
  2. Brennan, M.J. (1958). The supply of storage, American Economic Review 48, 50-72.Google Scholar
  3. Christiano, L.J. (1990). Linear-quadratic approximation and value-function iteration: A comparison, Journal of Business and Economic Statistics 8, 99-113.Google Scholar
  4. de Boor, C. (1978). A Practical Guide to Splines, New York, Springer-Verlag.Google Scholar
  5. Eckstein, Z. (1984). A rational expectations model of agricultural supply, Journal of Political Economics 92, 1-19.Google Scholar
  6. Eckstein, Z. (1985). The dynamic of agricultural supply: A reconsideration, American Journal of Agricultural Economics 67, 204-214.Google Scholar
  7. Gardner, B.L. (1979). Optimal Stockpiling of Grain. Lexington, Mass.: Lexington Books.Google Scholar
  8. Goodwin, T.H. and Sheffrin, S.M. (1982). Testing the rational expectations hypothesis in an agricultural market, Review of Economics and Statistics 64, 658-67.Google Scholar
  9. Gustafson, R.L. (1958). Carryover Levels for Grains:A method for determining amounts that are optimal under specified conditions. Technical Bulletin No. 1178, United States Department of Agriculture, Washington, D.C.Google Scholar
  10. Hansen, L.P. and Sargent, T.J. (1980). Formulating and estimating dynamic rational expectations models, Journal of Economic Dynamics and Control 2, 7-46.Google Scholar
  11. Johnson, S.A., Stedinger, J.R., Shoemaker, C.A., Li, Y. and Tejada-Guibert, J.A. (1993). Numerical solution of continuous-state dynamic programs using linear and spline interpolation, Operations Research 41, 484-500.Google Scholar
  12. Judd, K.L. (1991). Numerical Methods in Economics. Manuscript, Hoover Institution, Stanford University.Google Scholar
  13. Judd, K.L. (1992). Projection methods for solving aggregate growth models, Journal of Economic Theory 58, 410-452.Google Scholar
  14. Kaldor, N. (1939-40). Speculation and economic stability, Review of Economic Studies 7, 1-27.Google Scholar
  15. Kendrick, D. (1981). Stochastic Control for Economic Models, New York: McGraw-Hill.Google Scholar
  16. Lucas, R.E., Jr. (1981). Econometric policy evaluation: A critique, in studies in business cycle theory, Cambridge, Mass.: MIT Press.Google Scholar
  17. Miranda, M.J. (1986). A computable rational expectations model for agricultural price stabilization programs. Unpublished paper, The University of Wisconsin.Google Scholar
  18. Miranda, M.J. (1994). Maximum likelihood estimation of nonlinear rational expectations models by polynomial projection methods. Unpublished paper, The Ohio State University.Google Scholar
  19. Miranda, M.J. and Helmberger, P.G. (1988). The effects of price band buffer stock programs, American Economic Review 78, 46-58.Google Scholar
  20. Miranda, M.J. and Glauber, J.W. (1993). Estimation of dynamic nonlinear rational expectations models of primary commodity markets with private and government stockholding, Review of Economics and Statistics 75.Google Scholar
  21. Muth, J.F. (1961). Rational expectations and the theory of price movements, Econometrica 29, 315-35.Google Scholar
  22. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992). Numerical Recipes in FORTRAN:The art of scientific computing, 2nd ed. Cambridge: Cambridge University Press.Google Scholar
  23. Rausser, G.C. and Hochman, E. (1979). Dynamic Agricultural Systems: Economic Prediction and Control. New York: North-Holland.Google Scholar
  24. Scheinkman, J.A. and Schechtman, J. (1983). A simple competitive model with production and storage, Review of Economic Studies 50, 427-41.Google Scholar
  25. Tauchen, G. and Hussey, R. (1991). Quadrature-based methods for obtaining approximate solutions to nonlinear asset pricing models, Econometrica 59, 371-396.Google Scholar
  26. Taylor, J.B. and Uhlig, H. (1990). Solving nonlinear stochastic growth models: A comparison of alternative solution methods, Journal of Business and Economic Statistics 8, 1-18.Google Scholar
  27. Telser, L. (1958). Futures trading and the storage of cotton and wheat, Journal of Political Economics 66, 233-55.Google Scholar
  28. Williams, J.C. and Wright, B.D. (1991). Storage and Commodity Markets. New York: Cambridge University Press.Google Scholar
  29. Williams, J.B. (1935). Speculation and the carryover, Quarterly Journal of Economics 6.Google Scholar
  30. Working, H. (1948). Theory of the inverse carrying charge in futures markets, Journal of Farm Economics 30, 1-28.Google Scholar
  31. Working, H. (1949). The theory of price of storage, American Economic Review 39, 1254-62.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Mario J. Miranda
    • 1
  1. 1.Ohio State UniversityColumbusU.S.A.

Personalised recommendations

Cite article

Buy options