# Inference in heavy-tailed non-stationary multivariate time series

@inproceedings{Barigozzi2021InferenceIH, title={Inference in heavy-tailed non-stationary multivariate time series}, author={Matteo Barigozzi and Giuseppe Cavaliere and Lorenzo Trapani}, year={2021} }

We study inference on the common stochastic trends in a non-stationary, N -variate time series yt, in the possible presence of heavy tails. We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trendsm based on the eigenvalues of the sample second moment matrix of yt. We study the rates of such eigenvalues… Expand

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