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Guesstimation.

By Wojciech W. Charemza

Abstract

Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non-constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimisation of squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process. The numerical Monte Carlo examples are those of a regression problem and a dynamic disequilibrium model

Topics: estimation, shortdata series, macromodels, computations, methodology
Publisher: Dept. of Economics, University of Leicester
Year: 1998
OAI identifier: oai:lra.le.ac.uk:2381/4305

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Citations

  1. (1997a), ‘Simulation estimation of dynamic switching regression and dynamic disequilibirum models: some M onte Carlo results’,
  2. (1997b), ‘A smooth likelihood simulator for dynamic disequilibrium models’, doi
  3. (1991). and E.C.Prescott doi
  4. (1993). Applied general equilibrium modelling: applications, limitations and future development,
  5. (1994). Evaluation of parameters of LAM models’, paper presented at the seminar: LAM models: principles, constrution and first results,
  6. (1973). Evolutionstrategie: Optimierung technisher Systems nach Prinzipienderbiologishen Evolution,Fromman-Holzboog,
  7. (1994). Fundamentals of neural networks,Prentice doi
  8. (1992). Implementing stochastic optimal control of nonlinear models: a comparison with alternative solution methods’, Temi didiscussione del Servizio Studi No.
  9. (1994). LAM models for East European economies: general description’, paper presented at the seminar: LAM models: principles, constrution and first results,
  10. (1985). M acroeconometric disequilibrium models’,
  11. (1997). M acroeconomic forecasts for the Polish economy 1995-1996: a comparison’, paper presented at the US-Polish Economic Roundtable,
  12. (1993). Networks and chaos: statistical and probabilistic aspects Chapman and doi
  13. (1989). Neural computing: theory and practice,
  14. (1989). On the connection between neural network learning and multivariate nonlinear least squares estimation’,
  15. (1992). On the econometrics of world business cycles’, doi
  16. (1989). Optimal control, expectations and uncertainty Cambridge U.P., doi
  17. (1983). Quantitative economic policies and interactive planning, doi
  18. (1987). Stochastic models of control and economic dynamics,
  19. (1988). The econometrics of disequilibrium, doi
  20. (1996). The standard error of regressions’,
  21. (1982). Time to built and aggregate fluctuations’, doi
  22. (1990). Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks’, doi
  23. (1997). Zastosowanie algorytmów genetycznych w modelowaniu nieliniowychzale nociekonomicznych’,Ph.D. dissertation,

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