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Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model

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  • Ziping Zhao
  • Daniel P. Palomar

Abstract

In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships. The traditional analysis and estimation methodologies assume the underlying Gaussian distribution but, in practice, heavy-tailed data and outliers can lead to the inapplicability of these methods. In this paper, we propose a robust model estimation method based on the Cauchy distribution to tackle this issue. In addition, sparse cointegration relations are considered to realize feature selection and dimension reduction. An efficient algorithm based on the majorization-minimization (MM) method is applied to solve the proposed nonconvex problem. The performance of this algorithm is shown through numerical simulations.

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  • Ziping Zhao & Daniel P. Palomar, 2017. "Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model," Papers 1710.05513, arXiv.org.
    • Handle: RePEc:arx:papers:1710.05513
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    References listed on IDEAS

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    1. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
    4. Heino Bohn Nielsen, 2004. "Cointegration analysis in the presence of outliers," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 249-271, June.
    5. Franses, Philip Hans & Haldrup, Niels, 1994. "The Effects of Additive Outliers on Tests for Unit Roots and Cointegration," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 471-478, October.
    6. Lucas, André, 1995. "Unit Root Tests Based on M Estimators," Econometric Theory, Cambridge University Press, vol. 11(2), pages 331-346, February.
    7. Franses, Philip Hans & Lucas, Andre, 1998. "Outlier Detection in Cointegration Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 459-468, October.
    8. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    9. Franses, Philip Hans & Kloek, Teun & Lucas, Andre, 1998. "Outlier robust analysis of long-run marketing effects for weekly scanning data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 293-315, November.
    10. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    11. Lisha Chen & Jianhua Z. Huang, 2012. "Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1533-1545, December.
    12. Lucas, André, 1997. "Cointegration Testing Using Pseudolikelihood Ratio Tests," Econometric Theory, Cambridge University Press, vol. 13(2), pages 149-169, April.
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    Cited by:

    1. Ziping Zhao & Rui Zhou & Zhongju Wang & Daniel P. Palomar, 2018. "Optimal Portfolio Design for Statistical Arbitrage in Finance," Papers 1803.02974, arXiv.org.
    2. Ziping Zhao & Daniel P. Palomar, 2018. "Sparse Reduced Rank Regression With Nonconvex Regularization," Papers 1803.07247, arXiv.org.

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