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

Adriana Agapie ()

Working Papers of Institute for Economic Forecasting from Institute for Economic Forecasting

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.

Keywords: underground economy; Laffer curve; informal activity; fiscal policy; transitionmacroeconomic model; stochastic optimization; evolutionary algorithms; Repetitive Stochastic Guesstimation (search for similar items in EconPapers)
JEL-codes: C15 C65 E17 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2008-08
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.ipe.ro/RePEc/WorkingPapers/wpiecf080825.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rjr:wpiecf:080825

Access Statistics for this paper

More papers in Working Papers of Institute for Economic Forecasting from Institute for Economic Forecasting Contact information at EDIRC.
Bibliographic data for series maintained by Corina Saman ( this e-mail address is bad, please contact ).