PRIOR INFORMATION IN ECONOMETRIC GLOBAL OPTIMIZATION PROBLEMS: A BOOTSTRAP APPROACH
Adriana Agapie ()
Journal for Economic Forecasting, 2000, issue 4, 110-121
The paper analyzes the regression problem for small and undersized sample. Two classical algorithms are compared: Simulated Annealing (SA) versus Repetitive Stochastic Guesstimation (RSG). An improved version of RSG is built and compared to the previous two algorithms. The author concludes that a complete comparison among SA, RSG and RSGBOOT has to be done preliminary on every model to be estimated since these stochastic optimization algorithms are very sensitive to model specification.
Keywords: optimization algorithms; SA; RSG; RSGBOOT; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C01 C61 C8 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rjr:romjef:v::y:2000:i:4:p:110-121
Access Statistics for this article
Journal for Economic Forecasting is currently edited by Lucian Liviu Albu and Corina Saman
More articles in Journal 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 ).