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A0374
Title: Automated estimation of heavy-tailed vector error correction models Authors:  Feifei Guo - Hong Kong University of Science and Technology (Hong Kong) [presenting]
Abstract: It has been challenging to determine the co-integrating rank in the vector error correction (VEC) model when its noise is a heavy-tailed random vector. We propose an automated approach via adaptive shrinkage techniques to determine the co-integrating rank and estimate parameters simultaneously in the VEC model with unknown order $p$ when its noises are i.i.d. heavy-tailed random vectors with tail index $\alpha\in (0,2)$. It is shown that the estimated co-integrating rank and order $p$ equal to the true rank and the true order $p_{0}$, respectively, with probability tending to 1 as the sample size $n\to\infty$, while other estimated parameters achieve the oracle property, that is, they have the same rate of convergence and the same limiting distribution as those of estimated parameters when the co-integrating rank and the true order $p_{0}$ are known. We also propose a data-driven procedure for selecting the tuning parameters. Simulation studies are carried to evaluate the performance of this procedure in finite samples. The techniques are applied to explore the long-run and short-run behavior of prices of wheat, corn and wheat in the USA. The results may provide new insight into the Lasso approach for both stationary and non-stationary heavy-tailed time series.