# Non-monotonic penalizing for the number of structural breaks

## Author Info

• Erhard Reschenhofer

()

• David Preinerstorfer

()

• Lukas Steinberger

()

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## Abstract

This paper first reduces the problem of detecting structural breaks in a random walk to that of finding the best subset of explanatory variables in a regression model and then tailors various subset selection criteria to this specific problem. Of particular interest are those new criteria, which are obtained by means of simulation using the efficient algorithm of Bai and Perron (J Appl Econom 18:1–22, 2003 ). Unlike conventional variable selection methods, which penalize new variables entering a model either in the same way (e.g., AIC and BIC) or milder (e.g., MRIC and $$\mathrm {FPE}_\mathrm{{sub}}$$ ) than already included variables, they do not follow any monotonic penalizing scheme. In general, their non-monotonicity is more pronounced in the case of fat tails. The characteristics of the different criteria are illustrated using bootstrap samples from the Nile data set. Copyright Springer-Verlag Berlin Heidelberg 2013

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File URL: http://hdl.handle.net/10.1007/s00180-013-0419-4

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## Bibliographic Info

Article provided by Springer in its journal Computational Statistics.

Volume (Year): 28 (2013)
Issue (Month): 6 (December)
Pages: 2585-2598

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 Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2585-2598 Contact details of provider: Web page: http://www.springer.com Order Information: Web: http://www.springer.com/statistics/journal/180/PS2

## References

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1. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
2. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
3. Zeileis, Achim & Shah, Ajay & Patnaik, Ila, 2010. "Testing, monitoring, and dating structural changes in exchange rate regimes," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1696-1706, June.
4. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
5. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche 9807, Universite de Montreal, Departement de sciences economiques.
6. Zeileis, Achim & Kleiber, Christian & Krämer, Walter & Hornik, Kurt, 2002. "Testing and dating of structural changes in practice," Technical Reports 2002,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
7. Han Hong & Bruce Preston, 2008. "Bayesian Averaging, Prediction and Nonnested Model Selection," NBER Working Papers 14284, National Bureau of Economic Research, Inc.
8. anonymous, 1968. "Letters to the Editor," Management Science, INFORMS, vol. 15(4), pages B132-B136, December.
9. Perron, Pierre, 1997. "L’estimation de modèles avec changements structurels multiples," L'Actualité Economique, Société Canadienne de Science Economique, vol. 73(1), pages 457-505, mars-juin.
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