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SSRN Electronic Journal
Article . 2017 . Peer-reviewed
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Historical Decompositions for Nonlinear Vector Autoregression Models

Authors: Benjamin Wong;

Historical Decompositions for Nonlinear Vector Autoregression Models

Abstract

The historical decomposition is standard within the vector autogression (VAR) toolkit. It provides an interpretation of historical fluctuations in the modelled time series through the lens of the identified structural shocks. The proliferation of nonlinear VAR models naturally leads to extending the historical decomposition into nonlinear settings. This article discusses how to calculate an exact historical decomposition for a large class of popular nonlinear VAR models. In particular, the standard historical decomposition one obtains from a linear VAR is nested within the nonlinear case. The approach discussed in this article is sufficiently general to be relevant for many popular variants of nonlinear VAR models.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
bronze