Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@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