Skip to main content
Log in

Optimal budgetary and monetary policies under uncertainty: A stochastic control approach

  • Stochastic Models
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper, we determine optimal budgetary and monetary policies for Austria using a small macroeconometric model. We use a Keynesian model of the Austrian economy, called FINPOL1, estimated by ordinary least squares, which relates the main objective variables of Austrian economic policies, such as the growth rate of real gross domestic product, the rate of unemployment, the rate of inflation, the balance of payments, and the ratio of the federal budget deficit to GDP, to fiscal and monetary policy instruments, namely expenditures and revenues of the federal budget and money supply. Optimal fiscal and monetary policies are calculated for the model under a quadratic objective function using the algorithm OPTCON for the optimum control of nonlinear stochastic dynamic systems. Several control experiments are performed in order to assess the influence of different kinds of uncertainty on optimal budgetary and monetary policies. Apart from deterministic optimization runs, different assumptions about parameter uncertainties are introduced; the results of these different stochastic optimum control experiments are compared and interpreted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. Neck, Macroeconomic effects of Austrian budgetary policies: Simulation experiments with a small econometric model, in:Cybernetics and Systems Research '92, ed. R. Trappl (World Scientific, Singapore, 1992).

    Google Scholar 

  2. C.S. Tapiero,Applied Stochastic Models and Control in Management (North-Holland, Amsterdam, 1988).

    Google Scholar 

  3. C.S. Tapiero, Applicable stochastic control: From theory to practice, Euro. J. Oper. Res. 73(1994)209.

    Google Scholar 

  4. G.C. Chow,Analysis and Control of Dynamic Economic Systems (Wiley, New York, 1975).

    Google Scholar 

  5. G.C. Chow,Econometric Analysis by Control Methods (Wiley, New York, 1981).

    Google Scholar 

  6. D. Kendrick and P.A. Coomes, DUAL: A program for quadratic-linear stochastic control problems, Discussion Paper 84-15, Center for Economic Research, University of Texas, Austin (1984).

    Google Scholar 

  7. P.A. Coomes, PLEM: A computer program for passive learning, stochastic control experiments, J. Econ. Dyn. Contr. 11(1987)223.

    Google Scholar 

  8. E.C. MacRae, An adaptive learning rule for multiperiod decision problems, Econometrica 43(1975)893.

    Google Scholar 

  9. A.L. Norman, First order dual control, Ann. Econ. Social Measurement 5(1976)311.

    Google Scholar 

  10. D. Kendrick,Stochastic Control for Economic Models (McGraw-Hill, New York, 1981).

    Google Scholar 

  11. R. Neck and J. Matulka, Stochastic optimum control of macroeconometric models using the algorithm OPTCON, Euro. J. Oper. Res. 73(1994)384.

    Google Scholar 

  12. J. Matulka and R. Neck, OPTCON: An algorithm for the optimal control of nonlinear stochastic models, Ann. Oper. Res. 37(1992)375.

    Google Scholar 

  13. R. Neck and J. Matulka, Stochastic control of nonlinear economic models, in:New Directions in Computational Economics, ed. W.W. Cooper and A.B. Whinston (Kluwer, Dordrecht, 1994).

    Google Scholar 

  14. S. Karbuz, J. Matulka and R. Neck, OPTCON: An algorithm for the optimal control of nonlinear stochastic models: User Manual, Discussion Paper No. 284, Department of Economics, University of Bielefeld, Germany (1994).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Neck, R., Karbuz, S. Optimal budgetary and monetary policies under uncertainty: A stochastic control approach. Ann Oper Res 58, 379–402 (1995). https://doi.org/10.1007/BF02038862

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02038862

Keywords

Navigation