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Title: Restrictions search for panel VARs Authors:  Annika Schnuecker - DIW Berlin (Germany) [presenting]
Abstract: 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 stochastic search variable selection for PVAR models (SSVSP) is introduced as an alternative estimation procedure for PVARs. This extends the stochastic search specification selection ($S^4$) 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 $S^4$. Furthermore, this is validated by performing a forecast exercise for G7 countries.