Model selection via genetic algorithms illustrated with cross-country growth data

Empirical Economics

Volumn 33, Issue 2, 2007, Pages 313-337

  Acosta González, Eduardo ^a   Fernández Rodríguez, Fernando ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

Data mining; Genetic algorithms; Growth; Regressors selection

Indexed keywords

DATA MINING; ECONOMETRICS; ECONOMIC GROWTH; GENETIC ALGORITHM; GROWTH MODELING; HEURISTICS; NUMERICAL MODEL; REGRESSION ANALYSIS; SIMULATION;

EID: 34548064289     PISSN: 03777332     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00181-006-0104-3     Document Type: Article

References (29)

1. Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csake F (eds) 2nd international symposium on information theory, Budapest
2. Beenstock M, Szpiro G (2002) Specification search in nonlinear time-series models using the genetic algorithm. J Econ Dyn Control 26:811-835
3. Campos J, Hendry DF, Krolzig HM (2003) Consistent model selection by an automatic Gets approach. Oxf Bull Econ Stat 65:803-819
4. Dorsey RD, Mayer JM (1995) Genetic algorithms for estimation problems with multiple optima, nondiferentiability, and other irregular features. J Bus Econ Stat 13:53-66
5. Fernández C, Ley E, Steel MF (2001) Model uncertainty in cross-country growth regressions. J Appl Econ 16:563-576
6. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading
7. Hannan EJ, Quinn BG (1979) The determination of the order of an autoregression. J R Stat Soc B 41:190-195
8. Hansen BE (1999) Discussion of "data mining reconsidered". Econom J 2:192-201
9. Hendry DF, Krolzig HM (1999) Improving on "data mining reconsidered" by K.D. Hoover and S.J. Perez. Econom J 2:202-219
10. Hendry DF, Krolzig HM (2001) Automatic econometric model selection using PcGets 1.0. Timberlake Consultants Press, London
11. Hendry DF, Krolzig HM (2003) The properties of automatic gets modelling. Economics Department, Oxford University
12. Hendry DF, Krolzig H (2004) We ran one regression. Oxf Bull Econ Stat 66(5):799-810
13. Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
14. Hoover KD, Perez SJ (1999) Data mining reconsidered: Encompassing and the general- to-specific approach to specification search. Econom J 2:167-191
15. Hoover KD, Perez SJ (2004) Truth and robustness in cross-country growth regressions. Oxf Bull Econ Stat 66(5):765-798
16. King G, Honaker J, Joseph A, Scheve K (2001) Analyzing incomplete political science data: An alternative algorithm for multiple imputation. Am Polit Sci Rev 95:49-69
17. Koza J (1992) Genetic programming. MIT Press, Cambridge
18. Krolzig HM, Hendry DF (2001) Computer automation of general-to-specific model selection procedures. J Econ Dyn Control 25:831-866
19. Leamer EE (1983) Let's take the con out of econometrics. Am Econ Rev 73(3):31-43
20. Leamer EE (1985) Sensitivity analyses would help. Am Econ Rev 75(5):308-313
21. Levine R, Renelt D (1992) A sensitivity analysis of cross-country growth regressions. Am Econ Rev 82:942-963
22. Lovell MC (1983) Data mining. Rev Econ Stat 65:1-12
23. Mallows CL (1973) Some comments on C p. Technometrics 15:661-676
24. Miller A (2002) Subset selection in regression. Chapman & Hall/CRC, London
25. Perez-Amaral T, Gallo G M, White H (2003) A flexible tool for model building: The relevant transformation of the inputs network approach (RETINA). Oxf Bull Econ Stat 65:821-838
26. Sala-i-Martin XX (1997) I just ran two million regressions. Am Econ Rev 87:178-183
27. Schmertmann CP (1996) Functional search in economics using genetic programming. Comput Econ 9:275-298
28. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461-464
29. Szpiro GG (1997) A search for hidden relationship: Data mining with genetic algorithms. Comput Econ 10:267-277