System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach

International Journal of Forecasting

Volumn 25, Issue 4, 2009, Pages 734-743

  Orrell, David ^a   McSharry, Patrick ^b,c  

^a Physiomics PLC   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Decision making; Financial markets; Forecasting practice; Systems biology; Uncertainty

Indexed keywords

EID: 70349949639     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2009.05.002     Document Type: Article

References (38)

1. Anonymous (2005). NuTech Solutions Newsletter, 2, 3
2. Bachelier L. Théorie de la spéculation. Annales Scientifiques de l'École Normale Supérieure 3 17 (1900) 21-86
3. Barabási A. Linked: How everything is connected to everything else and what it means for business, science, and everyday life (2003), Plume (Penguin), Cambridge, MA
4. Barberis N., Shleifer A., and Vishny R. A model of investor sentiment. Journal of Financial Economics 49 (1998) 307-343
5. Bjerknes V. Dynamic meteorology and hydrography, Part II. Kinematics (1911), Carnegie Institute, Gibson Bros, New York
6. Bouchaud J.-P. Economics needs a scientific revolution. Nature 455 (2008) 1181
7. Chen Y., Zhu J., Lum P.Y., et al. Variations in DNA elucidate molecular networks that cause disease. Nature (2008) published online March 16, 2008
8. Daly H. Ecological economics and sustainable development, selected essays of Herman Daly (2007), Edward Elgar, Northampton, MA
9. Emilsson V., Thorleifsson G., Leonardson A.S., et al. Genetics of human gene expression and gene-gene transcriptional networks. Nature (2008) published online March 16, 2008
10. In: Ferber M.A., and Nelson J.A. (Eds). Beyond economic man: Feminist theory and economics (1993), Univ. of Chicago Press, Chicago
11. Gardner M. Mathematical Games: The fantastic combinations of John Conway's new solitaire game Life. Scientific American 223 (1970) 120-123
12. Goldstein, D. G., & Gigerenzer, G. (2009). Fast and frugal forecasting. International Journal of Forecasting, 25 (this issue)
13. Gopikrishnan P., Meyer M., Amaral L.A.N., and Stanley H.E. Inverse cubic law for the distribution of stock price variations. European Physical Journal B 3 (1998) 139-140
14. Gopikrishnan P., Plerou V., Amaral L.A.N., Meyer M., and Stanley H.E. Scaling of the distribution of fluctuations of financial market indices. Physical Review E 60 (1999) 5305-5316
15. Hood L. A personal journey of discovery: Developing technology and changing biology. Annual Review of Analytical Chemistry 1 (2008) 1-43
16. Ioannidis, J. (2009). Limits to forecasting in personalized medicine: An overview. International Journal of Forecasting (this issue)
17. Jaenicke R. Abundance of cellular material and proteins in the atmosphere. Science 308 (2005) 73
18. Kahneman D., and Tversky A. Prospect theory: An analysis of decision under risk. Econometrica XVLII (1979) 263-291
19. Kehoe, T. J. (2003). An evaluation of the performance of applied general equilibrium models of the impact of NAFTA. Staff Report 320, Federal Reserve Bank of Minneapolis
20. Kerr R.A. Storm-in-a-box forecasting. Science 304 (2004) 946-948
21. Lo A., and MacKinlay C. Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies 1 (1988) 41-66
22. Lux T. The stable Paretian hypothesis and the frequency of large returns: An examination of major German stocks. Applied Financial Economics 6 (1996) 463-475
23. Markowitz H.M. Portfolio Selection. Journal of Finance 7 1 (1952) 77-91
24. May R.M., Levin S.A., and Sugihara G. Ecology for bankers. Nature 451 (2008) 891-893
25. McCauley J.L. Dynamics of markets: Econophysics and finance (2004), Cambridge University Press, Cambridge
26. Meadows D.H., Meadows D.L., Randers J., and Behrens W.W. The limits to growth (1972), Universe Books, New York
27. Orrell D. The other side of the coin: The emerging vision of economics and our place in the world (2008), Key Porter, Toronto
28. Orrell D., Smith L., Barkmeijer J., and Palmer T.N. Model error in weather forecasting. Nonlinear Processes in Geophysics 8 (2001) 357-371
29. Plerou V., Gopikrishnan P., Amaral L.A.N., Meyer M., and Stanley H.E. Scaling of the distribution of price fluctuations of individual companies. Physical Review E 60 (1999) 6519-6529
30. Roebber P.J., Schultz D.M., Colle B.A., and Stensrud D.J. Towards improved prediction: High-resolution and ensemble modeling systems in operations. Weather Forecasting 19 (2004) 936-949
31. Roulston M.S., Kaplan D.T., Hardenberg J., and Smith L.A. Using medium-range weather forecasts to improve the value of wind energy production. Renewable Energy 28 (2003) 585-602
32. Samanidoul E., Zschischang E., Stauffer D., and Lux T. Agent-based models of financial markets. Reports on Progress in Physics 70 (2007) 409-450
33. Stainforth D., Aina T., Christensen C., et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433 (2005) 403-406
34. Sterman J.D. Business dynamics: Systems thinking and modeling for a complex world (2000), McGraw Hill
35. Surowiecki J. The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations (2004), Little, Brown, New York
36. Taylor, J. W., McSharry, P. E., & Buizza, R. Wind power density forecasting using ensemble predictions and time series models. IEEE Transactions on Energy Conversion (in press)
37. Tversky A., and Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5 (1992) 297-323
38. Wolfram S. A new kind of science (2002), Wolfram Media, Inc, Champaign, Illinois