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Non-monotonic penalizing for the number of structural breaks

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  • Erhard Reschenhofer
  • David Preinerstorfer
  • Lukas Steinberger

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

Suggested Citation

  • Erhard Reschenhofer & David Preinerstorfer & Lukas Steinberger, 2013. "Non-monotonic penalizing for the number of structural breaks," Computational Statistics, Springer, vol. 28(6), pages 2585-2598, December.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2585-2598
    DOI: 10.1007/s00180-013-0419-4
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    References listed on IDEAS

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    Cited by:

    1. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
    2. Marek Chudý & Erhard Reschenhofer, 2019. "Macroeconomic Forecasting with Factor-Augmented Adjusted Band Regression," Econometrics, MDPI, vol. 7(4), pages 1-14, December.

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