Genetically evolved models and normality of their fitted residuals

Journal of Economic Dynamics and Control

Volumn 25, Issue 11, 2001, Pages 1719-1749

  Kaboudan, M A ^a  

^a Mgmt Sci and Information Systems   (United States)

Author keywords

C45; C52; C63; Canadian lynx data; Genetic programming; Model evaluation; Sunspot numbers

Indexed keywords

EID: 0038414703     PISSN: 01651889     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1889(00)00004-X     Document Type: Article

References (42)

1. Akaike H. Information theory and an extension of the maximum likelihood principle. Petrov B., Caski F. Proceedings of the Second International Symposium on Information Theory. 1973;267-810 Akademia Kiado, Budapest.
2. Andrew M., Prager R. Genetic programming for the acquisition of double auction market strategies. Kinnear K.E. Jr. Advances in Genetic Programming. 1994;355-368 The MIT Press, Cambridge, MA.
3. Banzhaf, W., Nordin, P., Keller, R., Francone, F., 1998. Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco; dpunkt.verlag, Heidelberg.
4. Breusch T.S. Testing for autocorrelation in dynamic linear models. Australian Economic Papers. 17:1978;334-355.
5. Campbell M., Walker A. A survey of statistical work on the MacKenzie River series of annual Canadian lynx trappings from the year 1821-1934 and a new analysis (with discussion). Journal of Royal Statistical Society. A 140:1977;411-431.
6. Chen S., Yeh C., Lee W. Option pricing with genetic programming. Koza J., Banzhaf W., Chellapilla K., Deb K., Dorigo M., Fogel D., Garzon M., Goldberg D., Iba H., Riolo R. Genetic Programming 1998. Proceedings of the Third Annual Conference:1998;32-37 Morgan Kaufmann, San Francisco, CA.
7. Durbin J. Tests for serial correlation in regression analysis based on the periodogram of least squares residuals. Biometrika. 56:1969;1-15.
8. Durbin J. Testing for serial correlation in least-squares regression when some of the regressors are lagged dependent variables. Econometrica. 38:1970;410-421.
9. Elton C., Nicholson M. The ten-year cycle in numbers of the lynx in Canada. Journal of Animal Ecology. 11:1942;215-244.
10. Fogel D., Owens A., Walsh M. Artificial intelligence through simulated evolution. 1966;Wiley, New York.
11. Gabr M., Rao S. The estimation and prediction of subset bilinear time series with applications. Journal of Time Series Analysis. 2:1981;155-171.
12. Godfrey L.G. Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica. 46:1978;1293-1302.
13. Granger C., Andersen A. An Introduction to Bilinear Time Series Models. 1978;Vandenhoek and Ruprecht, Gottingen.
14. Hays W., Winkler R. Statistics. Probability, Inference, and Decision:1971;Holt, Rinehart & Winston, New York.
15. Henon M. A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics. 50:1976;69.
16. Hiden H., McKay B., Willis M., Tham M. Non-linear partial least squares using genetic programming. Koza J., Banzhaf W., Chellapilla K., Deb K., Dorigo M., Fogel D., Garzon M., Goldberg D., Iba H., Riolo R. Genetic Programming 1998. Proceedings of the Third Annual Conference:1998;128-133 Morgan Kaufmann, San Francisco, CA.
17. Hsieh D. Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business. 62:1989;339-368.
18. Iba, H., Sasaki, T., 1999. Using genetic programming to predict financial data. In: Proceedings of the 1999 Congress of Evolutionary Computation, July 6-9, Washington, DC, IEEE, Piscataway, NJ, pp. 244-251.
19. Jonsson P., Barklund J. Characterizing signal behavior using genetic programming. Fogarty T.C. Evolutionary Computing. Lecture Notes in Computer Science Vol. 1143:1996;62-72 Springer, Berlin.
20. Kaboudan, M., Vance, M., 1998. Statistical evaluation of symbolic regression forecasting of time-series. In: Proceedings of the Intentional Federation of Automatic Control Symposium on Computation in Economics, Finance and Engineering: Economic Systems, University of Cambridge, England, June 29 - July 1, 1998.
21. Kendall M., Stuart A. The Advanced Theory of Statistics. Vol. 2:1958;Hafner, New York.
22. Koza J. Genetic Programming. On the Programming of Computers by Means of Natural Selection:1992;The MIT Press, Cambridge, MA.
23. Lee D., Lee B., Chang S. Genetic programming model for long-term forecasting of electric power demand. Electric Power Systems Research. 40:1997;17-22.
24. Li W., McLeod A. ARMA modelling with non-Gaussian innovations. Journal of Time Series Analysis. 9:1988;155-168.
25. Liu T., Granger C., Heller W. Using the correlation exponent to detect whether an economic series is chaotic. Journal of Applied Econometrics. 7:1992;S25-S39.
26. Ljung G., Box G. On a measure of lack of fit in time series models. Biometrika. 65:1978;297-303.
27. Mackey M., Glass L. Oscillation and chaos in physiological control systems. Science. 197:1977;287.
28. May R. Simple mathematical models with very complicated dynamics. Nature. 261:1976;459-467.
29. McLeod A., Li W. Diagnostic checking ARMA time series models using squared residuals autocorrelation. Journal of Time Series Analysis. 4:1983;269-273.
30. Moran P. The statistical analysis of the Canadian lynx cycle. Australian Journal of Zoology. 1:1953;163-173.
31. Oakley H. Two scientific applications of genetic programming. stack filters and non-linear equation fitting to chaotic data Kinnear K.E. Jr. Advances in Genetic Programming. 1994;369-389 The MIT Press, Cambridge, MA.
32. Ozaki T. Non-linear threshold autoregressive models for non-linear random vibrations. Journal of Applied Probability. 18:1981;443-451.
33. Ozaki T. The statistical analysis of perturbed limit cycle processes using nonlinear time series models. Journal of Time Series Analysis. 3:1982;29-41.
34. Ozaki T. Non-linear time series models and dynamical systems. Hannan E., Krishnaiah P., Rao M. Handbook of Statistics. Vol. 5:1985;North-Holland, Amsterdam.
35. Pindyck R., Rubinfeld D. Econometric Models and Economic Forecasts. 1976;McGraw-Hill, New York.
36. Rade L., Westergren B. BETA, β, Mathematics Handbook. Concepts; Theorems; Methods; Algorithms; Formulas; Graphs; Tables:1990;Studentlitteratur, Lund, Sweden.
37. Rao S. On the theory of bilinear time series models. Journal of the Royal Statistical Society B. 43:1981;244-255.
38. Rao S., Chellapilla K. Evolving reduced parameter bilinear models for time series prediction using fast evolutionary programming. Koza J., Goldberg D., Fogel D., Riolo R. Genetic Programming. Proceedings of the First Annual Conference:1996;528-535 The MIT Press, Cambridge, MA.
39. Sathyanarayan S., Birru H., Chellapilla K. Evolving nonlinear time-series models using evolutionary programming. Proceedings of the 1999 Congress of Evolutionary Computation, July 6-9, Washington, DC. 1999;236-243 IEEE, Piscataway, NJ.
40. Singleton, A., 1994. Genetic programming with C++, Byte magazine, February. Software available from http://www.cen.uiuc.edu/~klipp/research/ssprob.Cxx/ and via anonymous ftp: node cs.ucl.ac.uk directory genetic/gp-code.
41. Tong H. Nonlinear Time Series Analysis. A Dynamical System Approach:1990;Oxford University Press, Oxford.
42. Waldmeier M. The Sunspot Activity in the years 1610-1960. 1961;Swiss Federal Observatory, Zurich.