As panel vector autoregressive (PVAR) models can include several countries and variables in one system, they are well suited for global spillover analyses. However, PVARs require restrictions to ensure the feasibility of the estimation. The present paper uses a selection prior for a data-based restriction search. It introduces the stochastic search variable selection for PVAR models (SSVSP) as an alternative estimation procedure for PVARs. This extends Koop and Korobilis's stochastic search specification selection (S4) to a restriction search on single elements. The SSVSP allows for incorporating dynamic and static interdependencies as well as cross-country heterogeneities. It uses a hierarchical prior to search for data-supported restrictions. The prior differentiates between domestic and foreign variables, thereby allowing a less restrictive panel structure. Absent a matrix structure for restrictions, a Monte Carlo simulation shows that SSVSP outperforms S4 in terms of deviation from the true values. Furthermore, the results of a forecast exercise for G7 countries demonstrate that forecast performance improves for the SSVSP specifications which focus on sparsity in form of no dynamic interdependencies.
model selection stochastic search variable selection PVAR