Stochastic Optimization in Econometric Models – A Comparison of GA, SA and RSG
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.
|Date of creation:||Aug 2008|
|Date of revision:|
|Contact details of provider:|| Postal: Casa Academiei, Calea 13, Septembrie nr.13, sector 5, Bucureşti 761172|
Phone: 004 021 3188148
Fax: 004 021 3188148
Web page: http://www.ipe.ro/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Dobrescu, Emilian, 1996. "Macromodels of the Romanian transition Economy," MPRA Paper 35810, University Library of Munich, Germany.
- Charemza, Wojciech W, 2002.
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 21(6), pages 417-33, September.
When requesting a correction, please mention this item's handle: RePEc:rjr:wpiecf:080825. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Corina Saman)
If references are entirely missing, you can add them using this form.