Assessing identifying restrictions in SVAR models

Arbeitspapier

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. I show that the data determine whether the probability mass in favour of the restrictions increases or not from prior to posterior. Using two applications, I find support for the restrictions used by Baumeister & Hamilton (2015a) in their two-equation model of labor demand and supply, and I find support for the true data generating process in a simulation exercise on the New Keynesian model.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: DIW Discussion Papers ; No. 1563

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Bayesian Analysis: General
• Thema: Identification ; Bayesian Econometrics ; Sign Restrictions

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Piffer, Michele
• Ereignis: Veröffentlichung
• (wer): Deutsches Institut für Wirtschaftsforschung (DIW)
• (wo): Berlin
• (wann): 2016

Weitere Informationen

• Handle: http://hdl.handle.net/10419/130213
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ