Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model

Ziping Zhao and Daniel P. Palomar

Papers from arXiv.org

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.

Date: 2017-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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