Improving inference and forecasting in VAR models using cross-sectional information

Arbeitspapier

We propose a prior for VAR models that exploits the panel structure of macroeconomic time series while also providing shrinkage towards zero to address overfitting concerns. The prior is flexible as it detects shared dynamics of individual variables across endogenously determined groups of countries. We demonstrate the usefulness of our approach via a Monte Carlo study and use our model to capture the hidden homo- and heterogeneities of the euro area member states. Combining pairwise pooling with zero shrinkage delivers sharper parameter inference that improves point and density forecasts over only zero shrinkage or only pooling specifications, and helps with structural analysis by lowering the estimation uncertainty.

Angaben zum Objekt

• ISBN: 978-3-96973-124-6
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Ruhr Economic Papers ; No. 960

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Thema: BVAR ; shrinkage ; forecasting ; structural analysis

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Prüser, Jan ; Blagov, Boris
• Ereignis: Veröffentlichung
• (wer): RWI - Leibniz-Institut für Wirtschaftsforschung
• (wo): Essen
• (wann): 2022

Weitere Informationen

• DOI: doi:10.4419/96973124
• Handle: http://hdl.handle.net/10419/264396
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ