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Inference on Structural Breaks using Information Criteria

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  • Alastair R. Hall
  • Denise R. Osborn
  • Nikolaos D. Sakkas

Abstract

This paper investigates the usefulness of information criteria for inference on the number of structural breaks in a standard linear regression model. In particular, we propose a modified penalty function for such criteria based on theoretical arguments, which implies each break is equivalent to estimation of three individual regression coefficients. A Monte Carlo analysis compares information criteria to sequential testing, with the modified BIC and HQIC criteria performing well overall, for DGPs both without and with breaks. The methods are also used to examine changes in Euro area monetary policy between 1971 and 2007.

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  • Alastair R. Hall & Denise R. Osborn & Nikolaos D. Sakkas, 2012. "Inference on Structural Breaks using Information Criteria," Centre for Growth and Business Cycle Research Discussion Paper Series 173, Economics, The University of Manchester.
    • Handle: RePEc:man:cgbcrp:173
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      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-09-27 06:33:00

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    Cited by:

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    2. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    3. Paraskevi Salamaliki, 2015. "Economic Policy Uncertainty and Economic Activity: A Focus on Infrequent Structural Shifts," Working Paper Series of the Department of Economics, University of Konstanz 2015-08, Department of Economics, University of Konstanz.
    4. Boldea, Otilia & Cornea-Madeira, Adriana & Hall, Alastair R., 2019. "Bootstrapping structural change tests," Journal of Econometrics, Elsevier, vol. 213(2), pages 359-397.
    5. Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2020. "Change point estimation in panel data with time‐varying individual effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 712-727, September.
    6. Bataa, Erdenebat & Izzeldin, Marwan & Osborn, Denise R., 2016. "Changes in the global oil market," Energy Economics, Elsevier, vol. 56(C), pages 161-176.
    7. Menezes, Rui & Oliveira, Álvaro & Portela, Sofia, 2019. "Investigating detrended fluctuation analysis with structural breaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 331-342.
    8. Francis X. Diebold & Glenn D. Rudebusch, 2019. "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," PIER Working Paper Archive 19-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Eduard Baumöhl & Štefan Lyócsa, 2014. "Risk-Return Convergence in CEE Stock Markets: Structural Breaks and Market Volatility," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(5), pages 352-373, November.
    10. Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2017. "The asymptotic behaviour of the residual sum of squares in models with multiple break points," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 667-698, October.
    11. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
    12. Baumöhl, Eduard & Lyócsa, Štefan, 2014. "Volatility and dynamic conditional correlations of worldwide emerging and frontier markets," Economic Modelling, Elsevier, vol. 38(C), pages 175-183.
    13. Bataa, Erdenebat & Osborn, Denise R. & Sensier, Marianne, 2018. "China's increasing global influence: Changes in international growth linkages," Economic Modelling, Elsevier, vol. 74(C), pages 194-206.
    14. Abdelati Abdelhamid & Nesrin Ozatac & Nigar Taspinar, 2023. "Investigating the Nexus between Energy Consumption and Financial Development via Considering Structural Breaks: Empirical Evidence from Argentina," Sustainability, MDPI, vol. 15(11), pages 1-14, May.
    15. Christie A Bahlai & Elise F Zipkin, 2020. "The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-16, January.

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