Controlling an Econometric Model Using Different Coefficient Sets

  • C.-L. Sandblom
  • H. A. Eiselt
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 3)


In optimisation experiments with macroeconometric models, there are many sources of uncertainty and error that should be taken into consideration. An issue which has received much attention during the last decade is how random disturbances in the model equations will affect optimal policies. Another issue and one which seems to have been overlooked is the effects of coefficient estimates of different refinement on the determination of optimal policies.


Optimal Policy Model Version Planning Period Payoff Matrix Historical Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Banasik, J.L. (1978), ‘A condensed and linearised version of the RDX2 model of the Canadian economy’ MBA thesis, Concordia University, Montreal.Google Scholar
  2. Banasik, J.L. (1978), ‘A condensed and linearised version of the RDX2 model of the Canadian economy’, MBA thesis, Concordia University, Montreal. Montreal (mimeo). Bank of Canada (1976), ‘The equations of RDX2 revised and estimated to 4Q72’, Bank of Canada. Technical Reports, No. 5.Google Scholar
  3. Chow, G.C. (1977), ‘Usefulness of imperfect models for the formulation of stabilisation policies’, Annals of Economic and Social Measurement, 6, pp. 175–187.Google Scholar
  4. Chow, G.C. (1979), ‘Effective use of econometric models in macroeconomic policy formulation’ in S. Holly, B. Rüstern, and M.B. Zarrop (eds.), Optimal Control for Econometric Models, MacMillan, London, pp. 31–39.Google Scholar
  5. Chow, G.C. (1981), Econometric Analysis by Control Methods, John Wiley and Sons, New York.Google Scholar
  6. Christ, C.F. (1975), ‘Judging the performance of econometric models of the U.S. economy’, International Economic Review, 16, pp. 54–74.CrossRefGoogle Scholar
  7. Drud, A. (1978), ‘An optimisation code for nonlinear econometric models based on sparse matrix techniques and reduced gradients’, Annals of Economic and Social Measurement, 6, pp. 563–580.Google Scholar
  8. Drud, A. and A. Meeraus (1980), ‘CONOPT — A system for large scale dynamic nonlinear optimisation — User’s manual’, Version 0.105, Development Research Center, World Bank, Washington, D.C.Google Scholar
  9. Rüstern, B. (1982), ‘Optimal policies with rival models: compromise solutions, game and minmax strategies’, Report EE.Con. 82.41, Imperial College of Science and Technology, University of London (mimeo).Google Scholar
  10. Sandblom, C.L. and J.L. Banasik (1981), ‘Optimal and suboptimal controls of a Canadian model’, in J.M.L. Janssen, L.F. Pau and A. Straszak (eds.), Dynamic Modelling and Control of National Economies, Pergamon Press, Oxford, pp. 71–78.Google Scholar
  11. Sandblom, C.L., J.L. Banasik and M. Parlar (1981), ‘Fxonomic policy with bounded controls’, Department of Quantitative Methods, Concordia University, Montreal (mimeo).Google Scholar
  12. Sandblom, C.L. and J.L. Banasik (1984), ‘Optimising economic policy with sliding windows’, Applied Economics, 16, pp. 45–56.CrossRefGoogle Scholar

Copyright information

© Martinus Nijhoff Publishers. Dordrecht/Boston/Lancaster 1984

Authors and Affiliations

  • C.-L. Sandblom
    • 1
  • H. A. Eiselt
    • 1
  1. 1.Concordia UniversityMontrealCanada

Personalised recommendations