Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/295170 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
DIW Discussion Papers No. 2081
Verlag: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Identification Through Heteroskedasticity
Stochastic Volatility
Non-centred Parameterisation
Shrinkage Prior
Normal Product Distribution
Tax Shocks
JEL: 
C11
C12
C32
E62
Dokumentart: 
Working Paper

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