Bayesian dynamic tensor regression (with Roberto Casarin, Matteo Iacopini and Sylvia Kaufmann)
Abstract: Tensor-valued data are becoming increasingly available in economics and this calls for suitable econometric tools. We propose a new dynamic linear model for tensor-valued response variables and covariates that encompasses some well-known econometric models as special cases. Our contribution is manifold. First, we define a tensor autoregressive process (ART), study its properties and derive the associated impulse response function. Second, we exploit the PARAFAC low-rank decomposition for providing a parsimonious parametrization and to incorporate sparsity effects. We also contribute to inference methods for tensors by developing a Bayesian framework which allows for including extra-sample information and for introducing shrinking effects. We apply the ART model to time-varying multilayer networks of international trade and capital stock and study the propagation of shocks across countries, over time and between layers.
Please note change of time to that previously advertised.
Sir Clive Granger BuildingUniversity of NottinghamUniversity Park
Nottingham, NG7 2RD
Connect with the University of Nottingham through social media and our blogs.
Campus maps | More contact information | Jobs