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THE Z-TRANSFORM AND COMPARATIVE DYNAMICS IN DISCRETE-TIME MODELS

Published online by Cambridge University Press:  08 December 2011

Liutang Gong*
Affiliation:
Peking University
Wei Wang
Affiliation:
Washington University in St. Louis
*
Address correspondence to: Gong Liutang, Guanghua School of Management, Peking University, Beijing, 100871, China; e-mail: [email protected].

Abstract

This paper develops a general technique for the computation of comparative dynamics in perfect-foresight discrete-time models. The method developed here is both applicable and general; it can be used to analyze the effects of the perturbation of parameters on endogenous variables and the welfare of an economic system derived from more general multisector models. It is neither restricted to the system's dimensions nor restricted by the assumption of distinct eigenvalues in the system.

Type
Articles
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Blanchard, O. and Kahn, C.M. (1980) The solution of linear difference models under rational expectations. Econometrica 48, 13051313.CrossRefGoogle Scholar
Buiter, W. (1984) Saddle-point problems in continuous time rational expectations models: A general method and some macroeconomic examples. Econometrica 52, 665680.CrossRefGoogle Scholar
Chamley, C. (1981) The welfare cost of capital income taxation in a growing economy. Journal of Political Economy 89, 468496.CrossRefGoogle Scholar
Cui, X. and Gong, L. (2006) Laplace transform methods for linearizing multidimensional systems. Economics Letters 90, 176182.Google Scholar
Cui, X., Gong, Liutang, Zhao, Xiaojun, and Zou, Heng-fu (2007) Z-Transform Method for Linearizing Multidimensional Discrete-Time Systems. Working paper, Peking University.Google Scholar
Doan, T.A. (2010) Practical issues with state-space models with mixed stationary and nonstationary dynamics. Technical Paper No. 2010-1.Google Scholar
Judd, K. (1982) An alternative to steady-state comparisons in perfect foresight models. Economics Letters 10, 5559.CrossRefGoogle Scholar
Judd, K. (1996) Approximation, perturbation and projection methods in economic analysis. In Amman, H., Kendrick, D., and Rust, J. (eds.), Handbook of Computational Economics, pp. 509585. Elsevier: North-Holland.Google Scholar
Juilliard, M. (1996) Dynare: A Program for the Resolution and Simulation of Dynamic Models with Forward Variables through the Use of a Relaxation Algorithm. CEPREMAP working paper 9602.Google Scholar
Laffargue, J.P. (2004) A sufficient condition for the existence and the uniqueness of a solution in macroeconomic models with perfect foresight. Journal of Economic Dynamics and Control 28, 19551975.CrossRefGoogle Scholar
Lucas, Robert E. (2003) Macroeconomic priorities. American Economic Review 93, 114.CrossRefGoogle Scholar
Meijdam, L. and Verhoeven, M. (1998) Comparative dynamics in perfect-foresight models. Computational Economics 12, 115124.CrossRefGoogle Scholar
Ortigueira, S. (1998) Fiscal policy in an endogenous growth model with human capital accumulation. Journal of Monetary Economics 42, 323355.CrossRefGoogle Scholar
Tahvonen, O. and Withagen, C. (1996) Optimality of irreversible pollution accumulation. Journal of Economic Dynamics and Control 20, 17751795.CrossRefGoogle Scholar
Tippett, M. and Warnock, T. (1997) The Garman–Ohlson structure system. Journal of Business Finance and Accounting 24 (7), 10751099.CrossRefGoogle Scholar
Turnovsky, S. (2000) Methods of Macroeconomic Dynamics. Boston: MIT Press.Google Scholar
Wirl, F. (2000) Optimal accumulation of pollution: Existence of limit cycles for the social optimum and the competitive equilibrium. Journal of Economic Dynamics and Control 24, 297306.CrossRefGoogle Scholar