Forecasting the Periodic Net Discount Rate with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  title =        "Forecasting the Periodic Net Discount Rate with
                 Genetic Programming",
  author =       "Neal F Wagner and Mark A Thompson",
  journal =      "Journal of Business Valuation and Economic Loss
  year =         "2009",
  volume =       "4",
  number =       "1",
  pages =        "Art. 4",
  publisher =    "The Berkeley Electronic Press",
  month =        oct # "~23",
  keywords =     "genetic algorithms, genetic programming, periodic net
                 discount rate, forecasts, Empirical and Conceptual",
  URL =          "",
  DOI =          "doi:10.2202/1932-9156.1072",
  bibsource =    "OAI-PMH server at",
  oai =          "",
  size =         "13 pages",
  abstract =     "This paper examines the periodic net discount rate
                 using genetic programming (GP) techniques to build
                 better short-term forecasts. Standard GP techniques
                 require human judgment as to which data window to use,
                 which may be problematic due to structural breaks and
                 persistence (or long memory) in the net discount rate.
                 We use a recently developed extension of GP to overcome
                 this problem. While our results show no significant
                 out-of-sample forecast improvement relative to the
                 linear alternative or random walk model over the full
                 sample, they do provide evidence as to the stochastic
                 nature of the net discount rate considering the AR(3)
                 model yielded lower forecasting errors in the post-1982
  notes =        "Neal F. Wagner, SolveIT Software Pty Ltd Mark A.
                 Thompson, Texas Tech University",

Genetic Programming entries for Neal Wagner Mark A Thompson