Simplicity, Inference and Modelling
Keeping it Sophisticatedly Simple

An inter-disciplinary perspective on the role of simplicity in modelling and inference, first published in 2002.

Arnold Zellner (Edited by), Hugo A. Keuzenkamp (Edited by), Michael McAleer (Edited by)

9780521121354, Cambridge University Press

Paperback, published 15 October 2009

316 pages
22.9 x 15.2 x 1.8 cm, 0.47 kg

"This lively and informative exposition of several points of view...will make this book pleasurable reading for not only philosophers of science and epistemologists, but also for those data analysts interested in formalizing the foundations that guide and shape their modeling practices." Journal of American Statistical Association

The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics.

1. The importance of simplicity in theory and practice A. Zellner, H. A. Keuzenkamp and M. McAleer
2. What is the problem of simplicity? E. Sober
3 Science seeks parsimony - not simplicity searching for pattern in phenomena H. Simon
4. A macroeconomic approach to complexity M. Boumans
5. The new science of simplicity M. R. Forster
6. What explains complexity? B. Hamminga
7. Occam's bonus A. W. F. Edwards
8. Simplicity, information, Kolmogorov complexity, and prediction P. Vitanyi and M. Li
9. Simplicity and statistical inference J. Rissanen
10. Rissanen's theorem and econometric series W. Ploberger and P. C. B. Phillips
11. Parametric versus non-parametric inference: satistical models and simplicity
12. The role of simplicity in an econometric model selection process A. Aznar, M. Isable Ayuda and C. Garcia-Olaverri
13. Simplicity in a behavioural, non-parametric context D. Tempelaar
14. Keep it sophisticatedly simple A. Zellner
15. Communication, complexity and coordination in games M. Ganslandt
16. The simplicity of an earnings frontier U. Jensen
17. Simplicity - views of the Nobel Laureates in economic science H. A. Keuzenkamp and M. McAleer.

Subject Areas: Philosophy of science [PDA], Economic statistics [KCHS], Econometrics [KCH]