Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model
This is an incremental improvement for econometrics and finance, addressing robustness and sparsity in cointegration analysis.
The paper tackles the problem of heavy-tailed data and outliers in vector error correction models by proposing a robust estimation method based on the Cauchy distribution, achieving feature selection through sparse cointegration relations.
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.