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Stochastic Optimization in Econometric Models – A Comparison of GA, SA and RSG

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  • Agapie, Adriana

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Abstract

This paper shows that, in case of an econometric model with a high sensitivity to data, using stochastic optimization algorithms is better than using classical gradient techniques. In addition, we showed that the Repetitive Stochastic Guesstimation (RSG) algorithm –invented by Charemza-is closer to Simulated Annealing (SA) than to Genetic Algorithms (GAs), so we produced hybrids between RSG and SA to study their joint behavior. The evaluation of all algorithms involved was performed on a short form of the Romanian macro model, derived from Dobrescu (1996). The subject of optimization was the model’s solution, as function of the initial values (in the first stage) and of the objective functions (in the second stage). We proved that a priori information help “elitist “ algorithms (like RSG and SA) to obtain best results; on the other hand, when one has equal believe concerning the choice among different objective functions, GA gives a straight answer. Analyzing the average related bias of the model’s solution proved the efficiency of the stochastic optimization methods presented.

Suggested Citation

  • Agapie, Adriana, 2008. "Stochastic Optimization in Econometric Models – A Comparison of GA, SA and RSG," Working Papers of Institute for Economic Forecasting 080825, Institute for Economic Forecasting.
  • Handle: RePEc:rjr:wpiecf:080825
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    File URL: http://www.ipe.ro/RePEc/WorkingPapers/wpiecf080825.pdf
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    References listed on IDEAS

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    1. Charemza, Wojciech W, 2002. "Guesstimation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(6), pages 417-433, September.
    2. Dobrescu, Emilian, 1996. "Macromodels of the Romanian transition Economy," MPRA Paper 35810, University Library of Munich, Germany.
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    More about this item

    Keywords

    underground economy; Laffer curve; informal activity; fiscal policy; transitionmacroeconomic model; stochastic optimization; evolutionary algorithms; Repetitive Stochastic Guesstimation;

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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