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When is nonfundamentalness in VARs a real problem? : An application to news shocks

Author: Paul Beaudry; Patrick Fève; Alain Guay; Franck Portier; National Bureau of Economic Research,
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2015.
Series: Working paper series (National Bureau of Economic Research), no. 21466.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
When the VAR representation of a times series has a non-fundamental representation, standard SVAR techniques cannot be used to exactly identify the effects of structural shocks. This problem is know to potentially arise when one of the structural shocks represents news about the future. However, as we shall show, in many case the non-fundamental representation of a time series may be very close to its fundamental  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Paul Beaudry; Patrick Fève; Alain Guay; Franck Portier; National Bureau of Economic Research,
OCLC Number: 919930570
Notes: "August 2015"
Description: 1 online resource (25 pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 21466.
Responsibility: Paul Beaudry, Patrick Fève, Alain Guay, Franck Portier.

Abstract:

When the VAR representation of a times series has a non-fundamental representation, standard SVAR techniques cannot be used to exactly identify the effects of structural shocks. This problem is know to potentially arise when one of the structural shocks represents news about the future. However, as we shall show, in many case the non-fundamental representation of a time series may be very close to its fundamental representation implying that standard SVAR techniques may provide a very good approximation of the effects of structural shocks even when the non-fundamentalness is formally present. This leads to the question: When is non-fundamentalness a real problem? In this paper we derive and illustrate a diagnostic based on a $R^2$ which provides a simple means of detecting whether non-fundamentalness is likely to be a quantitatively important problem in an applied settings. We use the identification of technological news shocks in US data as our running example.

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