Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@InProceedings{RePEc:sce:scecf3:44,

• author = "M. A. Kaboudan",
• title = "Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems",
• booktitle = "Computing in Economics and Finance",
• year = "2003",
• address = "University of Washington, Seattle, USA",
• month = "11-13 " # jul,
• organisation = "Society for Computational Economics",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/cef2003/Kaboudan_Extended_Abstract.pdf",
• abstract = "genetic programming (GP) is used in econometrics to predict US demand for natural gas using two recursive systems of equations. The first contains econometric models estimated using two-stage-least-squares (2SLS). These deliver estimates of policy-making parameters. The system contains four demand equations representing consuming sectors and an identity for total US. The second is to deliver forecasts of exogenous variables in the first using GP. GP can deliver relatively accurate predictions but its evolved equations are not useful in policy-making. For comparison, ARIMA models are used as input into the 2SLS system to compete with GP. Further, GP demand equations are evolved and used to obtain a different forecast altogether. The two forecasts are then compared with a forecast available from the US Department of Energy (DOE). Econometric and GP models deliver forecasts with different merits. Econometric models are concerned with estimating measures of interactions between a dependent variable and each of the independent variables. They provide for what if scenarios fundamental in policy-making that GP does not. The evolved equations are random combinations of variables and terminals that may not capture interactions between variables. Their forecasts may outperform those available using standard statistical techniques. Therefore, GP may add value to econometric models.",
• notes = "22 August 2004 http://ideas.repec.org/p/sce/scecf3/44.html CEF 2003",

}

Genetic Programming entries for Mahmoud A Kaboudan

Citations