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Model selection criteria in multivariate models with multiple structural changes

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  • Kurozumi, Eiji
  • Tuvaandorj, Purevdorj

Abstract

This paper considers the issue of selecting the number of regressors and the number of structural breaks in multivariate regression models in the possible presence of multiple structural changes. We develop a modified Akaike information criterion (AIC), a modified Mallows' Cp criterion and a modified Bayesian information criterion (BIC). The penalty terms in these criteria are shown to be different from the usual terms. We prove that the modified BIC consistently selects the regressors and the number of breaks whereas the modified AIC and the modified Cp criterion tend to overfit with positive probability. The finite sample performance of these criteria is investigated through Monte Carlo simulations and it turns out that our modification is successful in comparison to the classical model selection criteria and the sequential testing procedure robust to heteroskedasticity and autocorrelation.

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  • Kurozumi, Eiji & Tuvaandorj, Purevdorj, 2011. "Model selection criteria in multivariate models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 164(2), pages 218-238, October.
    • Handle: RePEc:eee:econom:v:164:y:2011:i:2:p:218-238
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    5. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
    6. Pierre Perron & Yohei Yamamoto & Jing Zhou, 2020. "Testing jointly for structural changes in the error variance and coefficients of a linear regression model," Quantitative Economics, Econometric Society, vol. 11(3), pages 1019-1057, July.
    7. Eo, Yunjong & Morley, James C., 2008. "Likelihood-Based Confidence Sets for the Timing of Structural Breaks," MPRA Paper 10372, University Library of Munich, Germany.
    8. Harris, David & Leybourne, Stephen J. & Taylor, A.M. Robert, 2016. "Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point," Journal of Econometrics, Elsevier, vol. 192(2), pages 451-467.
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    10. Gavard, Claire & Kirat, Djamel, 2020. "Short-term impacts of carbon offsetting on emissions trading schemes: Empirical insights from the EU experience," ZEW Discussion Papers 20-058, ZEW - Leibniz Centre for European Economic Research.
    11. Nakaota, Hiroshi & Fukuta, Yuichi, 2013. "The leading indicator property of the term spread and the monetary policy factors in Japan," Japan and the World Economy, Elsevier, vol. 28(C), pages 85-98.
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    14. Yaein Baek, 2018. "Estimation of a Structural Break Point in Linear Regression Models," Papers 1811.03720, arXiv.org, revised Jun 2020.
    15. Mikihito Nishi, 2024. "Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression," Papers 2406.14046, arXiv.org, revised Oct 2024.
    16. Djamel KIRAT & Claire GAVARD, 2020. "Short-term impacts of carbon offsetting on emissions trading schemes: empirical insights from the EU experience," LEO Working Papers / DR LEO 2821, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
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    18. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    19. Hiroshi Nakaota & Yuichi Fukuta, 2013. "The Leading Indicator Property of the Term Spread and the Monetary Policy Factors in Japan," Discussion Papers in Economics and Business 13-09, Osaka University, Graduate School of Economics, revised Jul 2013.

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    More about this item

    Keywords

    Structural breaks AIC Mallows' Cp BIC Information criteria;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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