Bayesian networks. A TSCM can be seen as a structural VAR identified by the causal relations among the variables. We classify … variables based on their observed time series data. It is shown that while an unconstrained VAR model does not imply any causal …

dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the … number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinite-dimensional VAR (or … IVAR) can be arbitrarily well characterized by a large number of finite-dimensional models in the spirit of the global VAR …

dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the … number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinite-dimensional VAR (or … IVAR) can be arbitrarily well characterized by a large number of finite-dimensional models in the spirit of the global VAR …

'theory first' versus the 'data first' perspectives in the context of a broader methodological framework with a view to …

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are … improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on … improve on VAR forecasts. …

economy. We use a two-stage non-recursive VAR model to identify monetary shocks. We construct then various overall monetary …