Partial identification of heteroskedastic structural VARs: Theory and Bayesian inference

Arbeitspapier

We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions under which the matrix containing structural parameters is partially or globally unique; (ii) a statistical procedure to assess the validity of the conditions mentioned above; and (iii) a shrinkage prior distribution for conditional variances centred on a hypothesis of homoskedasticity. Such a prior ensures that the evidence for identifying a structural shock comes only from the data and is not favoured by the prior. We illustrate our new methods using a U.S. fiscal structural model.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: DIW Discussion Papers ; No. 2081

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Hypothesis Testing: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Fiscal Policy
• Thema: Identification Through Heteroskedasticity ; Stochastic Volatility ; Non-centred Parameterisation ; Shrinkage Prior ; Normal Product Distribution ; Tax Shocks

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lütkepohl, Helmut ; Shang, Fei ; Uzeda, Luis ; Woźniak, Tomasz
• Ereignis: Veröffentlichung
• (wer): Deutsches Institut für Wirtschaftsforschung (DIW)
• (wo): Berlin
• (wann): 2024

Weitere Informationen

• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ