Detecting Shadow Economy Sizes with Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Philip D. Truscott and Michael F. Korns",
  title =        "Detecting Shadow Economy Sizes with Symbolic
  booktitle =    "Genetic Programming Theory and Practice IX",
  year =         "2011",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Jason H. Moore",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  publisher =    "Springer",
  chapter =      "11",
  pages =        "195--210",
  keywords =     "genetic algorithms, genetic programming, abstract
                 expression grammars, customised scoring, grammar
                 template genetic programming, universal form goal
  isbn13 =       "978-1-4614-1769-9",
  DOI =          "doi:10.1007/978-1-4614-1770-5_11",
  abstract =     "we examine the use of symbolic regression to tackle a
                 real world problem taken from economics: the estimation
                 of the size a country's 'shadow' economy. this is a
                 country's total monetary economic activity after
                 subtracting the official Gross Domestic Product. A wide
                 variety of methodologies are now used to estimate this.
                 Some have been criticised for an excessive reliance on
                 subjective predictive variables. Others use predictive
                 data that are not available for many developing
                 countries. we explore the feasibility of developing a
                 general-purpose regression formula using objective
                 development indicators. The dependent variables were
                 260 shadow economy measurements for various countries
                 from the period 1990-2006. Using 16 independent
                 variables, seven basis functions, and a depth of one
                 grammar level a search space of 1013 was created. we
                 focus on the power conferred by an abstract expression
                 grammar allowing the specification of a universal goal
                 formula with grammar depth control, and the
                 customisation of the scoring process that defines the
                 champion formula that 'survives' the evolutionary
                 process. Initial searching based purely on R-Squared
                 failed to produce plausible shadow economy estimates.
                 Later searches employed a customized scoring
                 methodology. This produced a good fit based on four
                 variables: GDP, energy consumption squared, this size
                 of the urban population, and the square of this figure.
                 The same formula produced plausible estimates for an
                 out of sample set of 510 countries for the years
                 2003-2005 and 2007. Though shadow economy prediction
                 will be controversial for some time to come, this
                 methodology may be the most powerful estimation formula
                 currently available for purposes that require
                 verifiable data and a single global formula.",
  notes =        "part of \cite{Riolo:2011:GPTP}",
  affiliation =  "Department of Information Systems and Computer
                 Science, Ateneo de Manila University, Loyola Hts,
                 Quezon City, Philippines",

Genetic Programming entries for Philip D Truscott Michael Korns