Si avvisa che in data 08-11-2018, alle ore 11:30 precise, presso l'Aula Seminari "F. Saleri" VI piano, Dipartimento di Matematica, Politecnico di Milano, si svolgerà il seguente seminario:

Relatore Matteo Iacopini, Università Ca’ Foscari Venezia

Titolo Bayesian Dynamic Tensor Regression

Sommario Multidimensional arrays (i.e. tensors) of data are becoming increasingly available and call for suitable econometric tools. We propose a new dynamic linear regression model for tensor-valued response variables and covariates that encompasses some well known multivariate models such as SUR, VAR, VECM, panel VAR and matrix regression models as special cases. For dealing with the over-parametrization and over-fitting issues due to the curse of dimensionality, we exploit a suitable parametrization based on the parallel factor (PARAFAC) decomposition which enables to achieve both parameter parsimony and to incorporate sparsity effects. Our contribution is twofold: first, we provide an extension of multivariate econometric models to account for both tensor-variate response and covariates; second, we show the effectiveness of proposed methodology in defining an autoregressive process for time-varying real economic networks. Inference is carried out in the Bayesian framework combined with Monte Carlo Markov Chain (MCMC). We show the efficiency of the MCMC procedure on simulated datasets, with different size of the response and independent variables, proving computational efficiency even with high-dimensions of the parameter space. Finally, we apply the model for studying the temporal evolution of real economic networks.

Tutti gli interessati sono invitati a partecipare.

Cordiali saluti, Laura Sangalli

-- Laura Maria Sangalli MOX - Dipartimento di Matematica Politecnico di Milano Piazza Leonardo da Vinci 32 20133 Milano - Italy tel: +39 02 2399 4554 fax: +39 02 2399 4568 email: laura.sangalli@polimi.itmailto:laura.sangalli@polimi.it url: http://mox.polimi.it/~sangalli