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BAYESIAN OCKHAM’S RAZOR AND NESTED MODELS

Published online by Cambridge University Press:  14 January 2019

Bengt Autzen Open the ORCID record for Bengt Autzen [Opens in a new window]
Bengt Autzen*
Affiliation:
Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universität München, Ludwigstrasse 31, 80539 München, Germany. Email: [email protected]. URL: www.mcmp.philosophie.uni-muenchen.de/people/faculty/autzen_bengt/index.html
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Abstract:

While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham’s razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham’s razor is applied to nested economic models. I will argue that previous responses to the problem found in the philosophical literature are unsatisfactory and develop a novel reply to the problem.

Keywords

Ockham’s razorBayesianismmodelsimplicityprior probability
Type
Article
Information
Economics & Philosophy , Volume 35 , Issue 2 , July 2019 , pp. 321 - 338
Copyright
Copyright © Cambridge University Press 2019 

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References

REFERENCES

Bils, M. and Klenow, P.J.. 2004. Some evidence on the importance of sticky prices. Journal of Political Economy 112: 947–985.Google Scholar
Bos, C.S., Mahieu, R.J. and Dijk, H.K. van. 2000. Daily exchange rate behaviour and hedging of currency risk. Journal of Applied Econometrics 15: 671–696.Google Scholar
Box, G.E.P. 1980. Sampling and Bayesian inference in scientific modelling and robustness. Journal of the Royal Statistical Society A 143: 383–430.Google Scholar
Burnham, K. and Anderson, D.. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York, NY: Springer.Google Scholar
Calvo, G.A. 1983. Staggered price setting in a utility-maximizing framework. Journal of Monetary Economics 12: 383–398.Google Scholar
Del Negro, M. and Schorfheide, F.. 2008. Forming priors for DSGE models and how it matters for nominal rigidities. Journal of Monetary Economics 55: 1191–1208.Google Scholar
Fagundes, N.J.R., Ray, N., Beaumont, M., Neuenschwander, S., Salzano, F.M., Bonatto, S.L. and Excoffier, L.. 2007. Statistical evaluation of alternative models of human evolution. Proceedings of the National Academy of Sciences USA 104: 17614–17619.Google Scholar
Forster, M. and Sober, E.. 1994. How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. British Journal for the Philosophy of Science 45: 1–37.Google Scholar
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B.. 2004. Bayesian Data Analysis. Boca Raton, FL: Chapman and Hall.Google Scholar
Henderson, L., Goodman, N.D., Tenenbaum, J.B. and Woodward, J.F.. 2010. The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. Philosophy of Science 77: 172–200.Google Scholar
Hong, Y. and Lee, T.H.. 2003. Inference on predictability of foreign exchange rates via generalized spectrum and nonlinear time series models. Review of Economics and Statistics 85: 1048–1062.Google Scholar
Hoogerheide, L. and Dijk, H.K. van. 2010. Bayesian forecasting of Value at Risk and Expected Shortfall using adaptive importance sampling. International Journal of Forecasting 26: 231–247.Google Scholar
Howson, C. 1988. On the consistency of Jeffreys’s simplicity postulate. Philosophical Quarterly 38: 68–83.Google Scholar
Jaynes, E.T. 2003. Probability Theory: The Logic of Science. Cambridge: Cambridge University Press.Google Scholar
Jefferys, W.H. and Berger, J.O.. 1992. Ockham’s razor and Bayesian analysis. American Scientist 80: 64–72.Google Scholar
Jeffreys, H. 1931. Scientific Inference. Cambridge: Cambridge University Press.Google Scholar
Kolmogorov, A.N. 1933. Grundbegriffe der Wahrscheinlichkeitsrechnung. Berlin: Springer.Google Scholar
Kriwoluzky, A. and Stoltenberg, C.A.. 2016. Nested models and model uncertainty. Scandinavian Journal of Economics 118: 324–353.Google Scholar
MacKay, D. 2003. Information Theory, Inference and Learning Algorithms. Cambridge: Cambridge University Press.Google Scholar
Popper, K.R. 1959. The Logic of Scientific Discovery. London: Hutchinson.Google Scholar
Romeijn, J.W. 2013. Abducted by Bayesians? Journal of Applied Logic 11: 430–439.Google Scholar
Romeijn, J.W. and Schoot, R. van de. 2008. A philosopher’s view on Bayesian evaluation of informative hypotheses. In Bayesian Evaluation of Informative Hypotheses, ed. Hoijtink, H., Klugkist, I. and Boelen, P.A., 329–357. New York, NY: Springer.Google Scholar
Sarno, L., Thornton, D.L. and Valente, G.. 2005. Federal funds rate prediction. Journal of Money, Credit and Banking 37: 449–472.Google Scholar
Smets, F. and Wouters, R.. 2007. Shocks and frictions in US business cycles: a Bayesian DSGE approach. American Economic Review 97: 586–606.Google Scholar
Sober, E. 2008. Evidence and Evolution: The Logic Behind the Science. Cambridge: Cambridge University Press.Google Scholar
Sober, E. 2015. Ockham’s Razor: A User’s Manual. Cambridge: Cambridge University Press.Google Scholar
Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and van der Linde, A.. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64: 583–639.Google Scholar
Templeton, A.R. 2010. Coherent and incoherent inference in phylogeography and human evolution. Proceedings of the National Academy of Sciences USA 107: 6376–6381.Google Scholar
Wrinch, D. and Jeffreys, H.. 1921. On certain fundamental principles of scientific inquiry. Philosophical Magazine 42: 369–390.Google Scholar