Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287860 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 44 [Issue:] 2 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2022 [Pages:] 223-246
Publisher: 
John Wiley & Sons, Ltd, Oxford, UK
Abstract: 
We represent the dynamic relation among variables in vector autoregressive (VAR) models as directed graphs. Based on these graphs, we identify so‐called strongly connected components. Using this graphical representation, we consider the problem of variable choice. We use the relations among the strongly connected components to select variables that need to be included in a VAR if interest is in impulse response analysis of a given set of variables. Our theoretical contributions show that the set of selected variables from the graphical method coincides with the set of variables that is multi‐step causal for the variables of interest by relating the paths in the graph to the coefficients of the ‘direct’ VAR representation. An empirical application illustrates the usefulness of the suggested approach: Including the selected variables into a small US monetary VAR is useful for impulse response analysis as it avoids the well‐known ‘price‐puzzle’.
Subjects: 
Directed graphs
impulse response analysis
multi‐step causality
variable selection
vector autoregression
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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