Some Experiences Applying Fuzzy Logic to Economics

  • Bárbara Díaz
  • Antonio Morillas
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 273)


Economy becomes a field of special interest for the application of fuzzy logic. Here we present some works carried out in this direction, highlighting their advantages and also some of the difficulties encountered. Fuzzy inference systems are very useful for Economic Modelling. The use of a rule system defines the underlying economic theory, and allows extracting inferences and predictions. We applied them to modelling and prediction of waged-earning employment in Spain, with Jang’s algorithm (ANFIS) for the period 1977-1998.

As additional experiences in this direction, we have applied the IFN algorithm (Info-Fuzzy- Network) developed by Maimon and Last to the study of the profit value of the Andalusian agrarian industry.

The search for key sectors in an economy has been and still is one of the more recurrent themes in Input-Output analysis, a relevant research area in the economic analysis. We proposed a multidimensional approach to classify the productive sectors of the Spanish Input- Output table. We subsequently analyzed the problems that can arise in key sector analysis and industrial clustering, due to the usual presence of outliers when using multidimensional data.


Fuzzy Logic Fuzzy Inference System Fuzzy Cluster Input Attribute Industrial Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barnett, V.: The ordering of multivariate data (with discussion). Journal of the Royal Statistical Society A 139, 318–354 (1976)CrossRefGoogle Scholar
  2. 2.
    Barnett, V., Lewis, T.: Outliers in Statistical Data. Wiley, Chichester (1994)zbMATHGoogle Scholar
  3. 3.
    Bauer, P.W., Berger, A.N., Humphrey, D.B.: Efficiency and productivity growth in U.S. banking. In: Fried, H.O., Lowell, C.A.K., Schmidt, S.S. (eds.) The measurement of productive efficiency: echniques and Applications, pp. 386–413. Oxford University Press (1993)Google Scholar
  4. 4.
    Belsley, D.A., Kuh, E., Welsch, R.E.: Regression Diagnostic: Identifying Influential Data and Sources of Collinearity. Wiley, New York (1980)zbMATHCrossRefGoogle Scholar
  5. 5.
    Berger, A.N., Humphrey, D.B.: The dominance of inefficiencies over scale and product mix economies in banking. Journal of Monetary Economics 20, 501–520 (1991)CrossRefGoogle Scholar
  6. 6.
    Berger, A.N., Humphrey, D.B.: Measurement and efficiency issues in commercial banking. In: Griliches, Z. (ed.) Output measurement in the service sectors. University of Chicago Press (1992)Google Scholar
  7. 7.
    Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2(3), 267–278 (1994)Google Scholar
  8. 8.
    Dang, X.: Nonparametric multivariate outlier detection methods with applications, PhD. Thesis, University of Dallas at Texas (2005)Google Scholar
  9. 9.
    De Mesnard, L.: On Boolean topological methods of structural analysis. In: Lahr, M.L., Dietzenbacher, E. (eds.) Input-Output Analysis: Frontiers and Extensions. Palgrave Macmillan, Basingstoke (2001)Google Scholar
  10. 10.
    Diaz, B.: Sistemas de inferencia difusos. Una aplicación al estudio del empleo asalriado en España. Phd Thesis (2000)Google Scholar
  11. 11.
    Diaz, B., Moniche, L., Morillas, A.: A fuzzy clustering approach to the key sectors of the Spanish economy. Economic Systems Research 18(3), 299–318 (2006)CrossRefGoogle Scholar
  12. 12.
    Dridi, C., Hewings, G.J.D.: Sectors associations and similarities in input-output systems: an application of dual scaling and fuzzy logic to Canada and the United States. Annals of Regional Science 37, 629–656 (2003)CrossRefGoogle Scholar
  13. 13.
    Espasa, A., Cancelo, J.R.: El cálculo del crecimiento de variables económicas a partir de modelos cuantitativos. Boletín Trimestral de Coyuntura 54, 65–84 (1994); INEGoogle Scholar
  14. 14.
    EUROSTAT: Ressources humaines en haute technologie. La mesure de l’emploi dans les secteurs manufacturiers de haute technologie; une perspective européenne. Serie Statistiques en bref. Recherche et développement, Monograph, vol. 8 (January 1998)Google Scholar
  15. 15.
    EUROSTAT: Employment in high technology manufacturing sectors at the regional level, Doc. Eurostat/A4/REDIS/103 (1998)Google Scholar
  16. 16.
    Feser, E.J., Bergman, E.M.: National industry cluster templates: a framework for applied regional cluster analysis. Regional Studies 34, 1–19 (2000)CrossRefGoogle Scholar
  17. 17.
    Filzmoser, P., Garrett, R.G., Reimann, C.: Multivariate outlier detection in exploration geochemistry. Computers and Geosciences 31, 579–587 (2005)CrossRefGoogle Scholar
  18. 18.
    Foster, G.: Financial statement analysis. Prentice Hall, New Jersey (1986)Google Scholar
  19. 19.
    Greene, W.H.: Econometric Analysis. Macmillan, New York (1993)Google Scholar
  20. 20.
    Hardin, J., Rocke, D.M.: Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. Computational Statistics and Data Analysis 44, 625–638 (2004)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Hardin, J., Rocke, D.M.: The distribution of robust distances. Journal of Computational and Graphical Statistics 14, 1–19 (2005)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Hathaway, R.J., Davenport, J.W., Bezdek, J.C.: Relational duals of the c-means algorithms. Pattern Recognition 22, 205–212 (1989), MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Hathaway, R.J., Bezdek, J.C.: NERF c-means: Non- Euclidean Relational Fuzzy Clustering. Pattern Recognition 27, 429–437 (1994)CrossRefGoogle Scholar
  24. 24.
    Central de Balances de Actividad empresarial en Andalucía. Instituto de Estadística de Andalucía,
  25. 25.
    INE, Tablas input-output de la Economía Española de 1995 (2000),
  26. 26.
    Contabilidad Nacional. Base (1995),
  27. 27.
    INEbase en la metodología expuesta en Investigación y desarrollo tecnológico. Indicadores de alta tecnología,
  28. 28.
    Jang, J.-S.R.: ANFIS: Adaptive Network-based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cibernetics 23(3), 665–685 (1993)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. Wiley, New York (1990)CrossRefGoogle Scholar
  30. 30.
    Kempthorne, P.J., Mendel, M.B.: Comments on Rousseeuw and Van Zomeren. Journal of the American Statistical Association 85, 647–648 (1990)CrossRefGoogle Scholar
  31. 31.
    Lantner, R.: Théorie de la Dominance Economique. Dunod, Paris (1974)Google Scholar
  32. 32.
    Lantner, R.: Influence graph theory applied to structural analysis. In: Lahr, M.L., Dietzenbacher, E. (eds.) Input-Output Analysis: Frontiers and Extensions. Palgrave Macmillan, Basingstoke (2001)Google Scholar
  33. 33.
    Last, M., Klein, Y., Kandel, A.: Knowledge Discovery in Time Series Databases. IEEE Transactions on Systems, Man and Cybernetics, Part B 31(1), 160–169 (2001)CrossRefGoogle Scholar
  34. 34.
    Little, R.J.A., Rubin, D.B.: Statistical analysis with missing data. John Wiley and Sons, New York (1987)zbMATHGoogle Scholar
  35. 35.
    Lopuhaä, H.P., Rousseeuw, P.J.: Breakdown points of affine equivariant estimators of multivariate location and covariance matrices. The Annals of Statistics 19, 229–248 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  36. 36.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  37. 37.
    Maimon, O., Last, M.: Knowledge Discovery and Data Mining – The Info-Fuzzy Network (IFN) Methodolog. Kluwer Academic Publishers, Netherlands (2000)zbMATHGoogle Scholar
  38. 38.
    Morillas, A.: La teoría de grafos en el análisis input-output. Secretariado de Publicaciones, Universidad de Málaga, Malaga (1983)Google Scholar
  39. 39.
    Morillas, A.: Indicadores topológicos de las características estructurales de una tabla input-output. Aplicación a la economía andaluza, Investigaciones Económicas 20, 103–118 (1983)Google Scholar
  40. 40.
    Morillas, A., Díaz, B.: Minería de datos y lógica difusa. Una aplicación al estudio de la rentabilidad económica de las empresas agroalimentarias en Andalucía. Revista Estadística Española, INE 46(157), 409–430 (2004)Google Scholar
  41. 41.
    Morillas, A., Díaz, B.: Key sectors, industrial clustering and multivariate outliers. Economic Systems Research 20(1), 57–73 (2008)CrossRefGoogle Scholar
  42. 42.
    Mougeot, M., Duru, G., Auray, J.-P.: La Structure Productive Francaise. Económica, Paris (1977)Google Scholar
  43. 43.
    Nagy, G.: State of the art in pattern recognition. Proceedings IEEE 56, 836–882 (1968)CrossRefGoogle Scholar
  44. 44.
    Oosterhaven, J., Stelder, D.: Net multipliers avoid exaggerating impacts: with a bi-regional illustration for the Dutch transportation sector. Journal of Regional Science 42, 533–543 (2002)CrossRefGoogle Scholar
  45. 45.
    Oosterhaven, J.: On the definition of key sectors and the stability of net versus gross multipliers, SOM Research Report 04C01, Research Institute SOM, University of Groningen, The Netherlands (2004),
  46. 46.
    Rey, S., Mattheis, D.: Identifying Regional Industrial Clusters in California. Volumes I-III, Reports prepared for the California Employment Development Department, San Diego State University (2000)Google Scholar
  47. 47.
    Rossier, E.: Economie Structural, Económica, Paris (1980)Google Scholar
  48. 48.
    Rousseeuw, P.J.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational Applied Mathematics 20, 53–65 (1987)zbMATHCrossRefGoogle Scholar
  49. 49.
    Rousseeuw, P.J., Leroy, A.M.: Robust regression and outliers detection. Wiley, New York (1987)CrossRefGoogle Scholar
  50. 50.
    Rousseeuw, P.J., van Zomeren, B.C.: Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association 85, 633–639 (1990)CrossRefGoogle Scholar
  51. 51.
    Rousseeuw, P.J., Hubert, M.: Recent developments in PROGRESS. In: Dodge, Y. (ed.) L1-Statistical Procedures and Related Topics. IMS Lecture Notes, vol. 31, pp. 201–214 (1997)Google Scholar
  52. 52.
    Rousseeuw, P.J., van Driessen, K.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223 (1999)CrossRefGoogle Scholar
  53. 53.
    Rubin, D.B.: Inference and missing data. Biometrika 63, 581–592 (1976)MathSciNetzbMATHCrossRefGoogle Scholar
  54. 54.
    Ruspini, E.: A new approach to clustering. Information and Control 15, 22–32 (1969)zbMATHCrossRefGoogle Scholar
  55. 55.
    Sugeno, M., Yasukawa, T.: Fuzzy-Logic Based Approach to qualitative modelling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)CrossRefGoogle Scholar
  56. 56.
    Tanaka, K., Sano, M., Watanabe, H.: Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique. IEEE Transactions on Fuzzy Systems 3(3), 271–279 (1995)CrossRefGoogle Scholar
  57. 57.
    Yager, R.R., Filev, D.P.: Approximate clustering via the mountain method. IEEE Transactions on Systems, Man and Cibernetics 24, 209–219 (1994)Google Scholar
  58. 58.
    Zadeh, L.A.: A New Direction in AI - Toward a Computational Theory of Perceptions. AAAI Magazine, 73–84 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bárbara Díaz
    • 1
  • Antonio Morillas
    • 2
  1. 1.Statistics and Econometrics DepartmentUniversity of MalagaMalagaSpain
  2. 2.Applied Economics (Statistics and econometrics) DepartmentUniversity of MalagaMalagaSpain

Personalised recommendations