Correlations Versus Causality Approaches to Economic Modeling

  • Tshilidzi Marwala
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter explores the issue of treating a predictive system as a missing data problem i.e. correlation exercise and compares it to treating as a cause and effect exercise, that is, feed-forward network. An auto-associative neural network is combined with genetic algorithm and then applied to missing economic data estimation. The algorithm is used on data that contain ten economic variables. The results of the missing data imputation approach are compared to those from a feed-forward neural network.

Keywords

Manifold Ozone Income Schizophrenia 

References

  1. Abdella M, Marwala T (2005a) The use of genetic algorithms and neural networks to approximate missing data in database. In: Proceedings of the IEEE 3rd international conference on computational cybernetics, Mauritius, 2005, pp 207–212Google Scholar
  2. Abdella M, Marwala T (2005b) Treatment of missing data using neural networks. In: Proceedings of the IEEE international joint conference on neural networks, Montreal, 2005, pp 598–603Google Scholar
  3. Abdoullaev A (2000) The ultimate of reality: reversible causality. In: Proceedings of the 20th world congress of philosophy, Philosophy Documentation Centre, Boston, internet site, paideia project on-line. http://www.bu.edu/wcp/MainMeta.htm. Last accessed 3 Jan 2013
  4. Achkar R, Owayjan M, Mrad C (2011) Landmine detection and classification using MLP. In: Proceedings of the 3rd international conference on computational intelligence, modeling and simulation, Langkawi, 2011, pp 1–6Google Scholar
  5. Akkemik KA, Göksal K, Li J (2012) Energy consumption and income in Chinese provinces: heterogeneous panel causality analysis. Appl Energy 99:445–454CrossRefGoogle Scholar
  6. Akkoç S (2012) An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: the case of turkish credit card data. Eur J Oper Res 222:168–178CrossRefGoogle Scholar
  7. Ashley R, Ye H (2012) On the Granger causality between median inflation and price dispersion. Appl Econ 44:4221–4238CrossRefGoogle Scholar
  8. Aydilek IB, Arslan A (2012) A novel hybrid approach to estimating missing values in databases using K-nearest neighbors and neural networks. Int J Innov Comput Inf Control 8:4705–4717Google Scholar
  9. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordGoogle Scholar
  10. Blanco A, Pino-Mejías R, Lara J, Rayo S (2013) Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst Appl 40:356–364CrossRefGoogle Scholar
  11. Boddy LM, Thomas NE, Fairclough SJ, Tolfrey K, Brophy S, Rees A, Knox G, Baker JS, Stratton G (2012) ROC generated thresholds for field-assessed aerobic fitness related to body size and cardiometabolic risk in schoolchildren. doi: 10.1371/journal.pone.0045755, http://pubget.com/paper/23029224. Last accessed 7 March 2013
  12. Browne A, Jakary A, Vinogradov S, Fu Y, Deicken RF (2008) Automatic relevance determination for identifying thalamic regions implicated in schizophrenia. IEEE Trans Neural Netw 19:1101–1107CrossRefGoogle Scholar
  13. Cette G, de Jong M (2013) Breakeven inflation rates and their puzzling correlation relationships. Appl Econ 45:2579–2585CrossRefGoogle Scholar
  14. Chen M-H (2010) Pattern recognition of business failure by autoassociative neural networks in considering the missing values. In: Proceedings of the international computer symposium, Taipei, Taiwan, pp 711–715Google Scholar
  15. Chen GG, Åstebro T (2012) Bound and collapse Bayesian reject inference for credit scoring. J Oper Res Soc 63:1374–1387CrossRefGoogle Scholar
  16. Chen C-M, Yeh C-Y (2012) The causality examination between demand uncertainty and hotel failure: a case study of international tourist hotels in Taiwan. Int J Hosp Manag 31:1045–1049CrossRefGoogle Scholar
  17. Correa AB, Gonzalez AM (2011) Evolutionary algorithms for selecting the architecture of a MLP neural network: a credit scoring case. In: Proceedings of the IEEE international conference on data mining, Vancouver, 2011, pp 725–732Google Scholar
  18. Davis S (2012) Choosing the right baskets for your eggs: deriving actionable customer segments using supervised genetic algorithms. Int J Mark Res 54:689–698CrossRefGoogle Scholar
  19. Daya B, Akoum AH, Bahlak S (2012) Geometrical features for multiclass vehicle type recognition using MLP network. J Theory Appl Inf Technol 43:285–294Google Scholar
  20. Dragomir OE, Dragomir F, Brezeanu I, Mincǎ E (2011) MLP neural network as load forecasting tool on short-term horizon. In: Proceedings of the 19th Mediterranean conference on control and automation, Corfu, Greece, pp 1265–1270Google Scholar
  21. Dubey HC, Nandita, Tiwari AK (2012) Blind modulation classification based on MLP and PNN. In: Proceedings of the students conference on engineering and systems, Utter Paredesh, India, art. no. 6199042Google Scholar
  22. Ebrahimpour R, Nikoo H, Masoudnia S, Yousefi MR, Ghaemi MS (2011) Mixture of MLP-experts for trend forecasting of time series: a case study of the Tehran stock exchange. Int J Forecast 27:804–816CrossRefGoogle Scholar
  23. García V, Marqués AI, Sánchez JS (2012) Non-parametric statistical analysis of machine learning methods for credit scoring. Adv Intell Syst Comput 171:263–272CrossRefGoogle Scholar
  24. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438CrossRefGoogle Scholar
  25. Green C (2003) The lost cause: causation and the mind-body problem. Oxford Forum, OxfordGoogle Scholar
  26. Hacker RS, Hatemi JA (2006) Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application. Appl Econ 38:1489–1500CrossRefGoogle Scholar
  27. Horner JK (2005) An automatic relevance determination method for identifying the signature of stage I ovarian cancer in the mass-spectrum of serum proteins. In: Proceedings of the 2005 international conference on artificial intelligence, Las Vegas, Nevada, pp 620–626Google Scholar
  28. Kramer MA (1992) Autoassociative neural networks. Comput Chem Eng 16:313–328CrossRefGoogle Scholar
  29. Kürüm E, Yildirak K, Weber G-W (2012) A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve. Cent Eur J Oper Res 20:529–557CrossRefGoogle Scholar
  30. Li Y, Campbell C, Tipping M (2002) Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics 18:1332–1339CrossRefGoogle Scholar
  31. Lin Y-J (2012) Comparison of CART- and MLP-based power system transient stability preventive control. Int J Electr Power Energy Syst 45:129–136CrossRefGoogle Scholar
  32. Liu L, Wan J (2012) The relationships between Shanghai stock market and CNY/USD exchange rate: new evidence based on cross-correlation analysis, structural cointegration and nonlinear causality test. Physica A Stat Mech Appl 391:6051–6059CrossRefGoogle Scholar
  33. Liu J, Gao H, Liu H (2012) Study of optimum solution about aviation ammunition transport problem based on genetic algorithm. In: Proceedings of the IEEE international conference on service operations and logistics, and informatics, Suzhou, China, pp 308–311Google Scholar
  34. Lorimer L, Gemmell HG, Sharp PF, McKiddie FI, Staff RT (2012) Improvement in DMSA imaging using adaptive noise reduction: an ROC analysis. Nucl Med Commun 33:1212–1216CrossRefGoogle Scholar
  35. Lu J, Humphreys P, McIvor R, Maguire L, Wiengarten F (2012) Applying genetic algorithms to dampen the impact of price fluctuations in a supply chain. Int J Prod Res 50:5396–5414CrossRefGoogle Scholar
  36. MacKay DJC (1991) Bayesian methods for adaptive models. Ph.D. thesis, California Institute of Technology, PasadenaGoogle Scholar
  37. MacKay DJC (1992) A practical Bayesian framework for back propagation networks. Neural Comput 4:448–472CrossRefGoogle Scholar
  38. Marwala T (2009) Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques. IGI Global Publications, New YorkCrossRefGoogle Scholar
  39. Marwala T (2012) Condition monitoring using computational intelligence methods. Springer, LondonCrossRefGoogle Scholar
  40. Marwala T, Chakraverty S (2006) Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm. Curr Sci 90:542–548Google Scholar
  41. Meng Y (2012) Measuring intelligent false alarm reduction using an ROC curve-based approach in network intrusion detection. In: Proceedings of the IEEE international conference on computational intelligence for measurement systems and applications, Tianjin, China, pp 108–113Google Scholar
  42. Mistry J, Nelwamondo FV, Marwala T (2009) Investigating demographic influences for HIV classification using Bayesian autoassociative neural networks. Lect Note Comput Sci 5507:752–759CrossRefGoogle Scholar
  43. Miyazaki T, Hamori S (2013) Testing for causality between the gold return and stock market performance: evidence for ‘gold investment in case of emergency’. Appl Finance Econ 23:27–40CrossRefGoogle Scholar
  44. Mutlu Ö, Polat O, Supciller AA (2013) An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II. Comput Oper Res 40:418–426CrossRefGoogle Scholar
  45. Narayanan S, Marks RJ II, Vian JL, Choi JJ, El-Sharkawi MA, Thompson BB (2002) Set constraint discovery: missing sensor data restoration using auto-associative regression machines. Proc Int Jt Conf Neural Netw 3:2872–2877Google Scholar
  46. Patilea V, Raïssi H (2012) Adaptive estimation of vector autoregressive models with time-varying variance: application to testing linear causality in mean. J Stat Plan Inference 142:2871–2890CrossRefGoogle Scholar
  47. Pearl J (2000) Causality: models of reasoning and inference. Cambridge University Press, CambridgeMATHGoogle Scholar
  48. Pereira LFA, Pinheiro HNB, Silva JIS, Silva AG, Pina TML, Cavalcanti GDC, Ren TI, De Oliveira JPN (2012) A fingerprint spoof detection based on MLP and SVM. In: Proceedings of the international IEEE joint conference on neural networks. Brisbane, Australia, 2012, art. no. 6252582Google Scholar
  49. Peyghami MR, Khanduzi R (2012) Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network. Neural Comput Appl 21:125–132CrossRefGoogle Scholar
  50. Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27:221–234CrossRefGoogle Scholar
  51. Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San MateoGoogle Scholar
  52. Ramírez Palencia AE, Mejía Delgadillo GE (2012) A computer application for a bus body assembly line using genetic algorithms. Int J Prod Econ 140:431–438CrossRefGoogle Scholar
  53. Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theory Appl Climatol 109:407–414CrossRefGoogle Scholar
  54. Salazar DSP, Adeodato PJL, Arnaud AL (2012) Data transformations and seasonality adjustments improve forecasts of MLP ensembles. In: Proceedings of the IEEE conference on evolving and adaptive intelligent systems, Madrid, Spain, pp 139–144Google Scholar
  55. Sermpinis G, Dunis C, Laws J, Stasinakis C (2012) Forecasting and trading the EUR/USD exchange rate with stochastic neural network combination and time-varying leverage. Decis Support Syst 54:316–329CrossRefGoogle Scholar
  56. Sioud A, Gravel M, Gagné C (2012) A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times. Comput Oper Res 39:2415–2424MathSciNetMATHCrossRefGoogle Scholar
  57. Smyrnakis MG, Evans DJ (2007) Classifying ischemic events using a Bayesian inference multilayer perceptron and input variable evaluation using automatic relevance determination. Comput Cardiol 34:305–308Google Scholar
  58. Tang B, Qiu S (2012) A new credit scoring method based on improved fuzzy support vector machine. In: Proceedings of the IEEE international conference on computer science and automation engineering, pp 73–75Google Scholar
  59. Tang LL, Liu A, Chen Z, Schisterman EF, Zhang B, Miao Z (2012) Nonparametric ROC summary statistics for correlated diagnostic marker data. Stat Med. doi: 10.1002/sim.5654
  60. Tiwari AK (2012) Causality between: an empirical investigation in the frequency domain. Indian Growth Dev Rev 5:151–172CrossRefGoogle Scholar
  61. Turchenko V, Beraldi P, De Simone F, Grandinetti L (2011) Short-term stock price prediction using MLP in moving simulation mode. In: Proceedings of the 6th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications, Prague, 2011, pp 666–671Google Scholar
  62. Van Calster B, Timmerman D, Nabney IT, Valentin L, Van Holsbeke C, Van Huffel S (2006) Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: a multi-center study. Proc Annu Int Conf IEEE Eng Med Biol Soc 1:5342–5345Google Scholar
  63. Vedala R, Kumar BR (2012) An application of Naive Bayes classification for credit scoring in e-lending platform. In: Proceedings of the international conference on data science and engineering, Kerala, India, pp 81–84Google Scholar
  64. Vidal T, Crainic TG, Gendreau M, Prins C (2013) A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Comput Oper Res 40:475–489MathSciNetCrossRefGoogle Scholar
  65. Vieira A, Ribeiro B, Chen N (2012) Credit scoring for SME using a manifold supervised learning algorithm. Lect Note Comput Sci 7435:763–770CrossRefGoogle Scholar
  66. Vladislavleva E, Friedrich T, Neumann F, Wagner M (2012) Predicting the energy output of wind farms based on weather data: important variables and their correlation. Renew Energy 50:236–243CrossRefGoogle Scholar
  67. Wang D, Lu W-Z (2006) Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model. Chemosphere 62:1600–1611CrossRefGoogle Scholar
  68. Wang L, Zhang H, Li W (2012) Analysis of causality between tourism and economic growth based on computational econometrics. J Comput 7:2152–2159Google Scholar
  69. Wesseh PK Jr, Zoumara B (2012) Causal independence between energy consumption and economic growth in Liberia: evidence from a non-parametric bootstrapped causality test. Energy Policy 50:518–527CrossRefGoogle Scholar
  70. Yuan T, Joe Qin S (2012) Root cause diagnosis of plant-wide oscillations using granger causality. IFAC Proc Vol (IFAC-Pap Online) 8(Part 1):160–165Google Scholar
  71. Zieba M, Światek J (2012) Ensemble classifier for solving credit scoring problems. IFIP Adv Inf Commun Technol 372:59–66CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Tshilidzi Marwala
    • 1
  1. 1.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations