Restrictions Search for Panel VARs

Konferenzbeitrag

Panel vector autoregressive (PVAR) models can include several countries and variables in one system and thus 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's and Korobilis's stochastic search specification selection (S4) to a restriction search on single elements. The SSVSP allows to incorporate 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. Furthermore, this is validated by performing a forecast exercise for G7 countries.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel - Session: Panel Macroeconometrics ; No. C01-V1

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Model Evaluation, Validation, and Selection

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schnücker, Annika
• Event: Veröffentlichung
• (who): ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
• (where): Kiel und Hamburg
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/145566
• Last update: 10.03.2025, 11:43 AM CET