Prior selection for vector autoregressions

Arbeitspapier

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: ECB Working Paper ; No. 1494

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: Bayesian methods ; forecasting ; hierarchical modeling ; Impulse responses ; marginal likelihood

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Giannone, Domenico ; Lenza, Michele ; Primiceri, Giorgio E.
• Ereignis: Veröffentlichung
• (wer): European Central Bank (ECB)
• (wo): Frankfurt a. M.
• (wann): 2012

Weitere Informationen

• Handle: http://hdl.handle.net/10419/153927
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ