Directed Graphs and Variable Selection in Large Vector Autoregressive Models

Dominik Bertsche (), Ralf Brüggemann and Christian Kascha ()
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Dominik Bertsche: Department of Economics, Box 129, 78457 Konstanz, Germany

No 2018-08, Working Paper Series of the Department of Economics, University of Konstanz from Department of Economics, University of Konstanz

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 (SCCs). Using this graphical representation, we consider the problem of variable selection. We use the relations among the strongly connected components to select variables that need to be included in a VAR if interest is in forecasting or impulse response analysis of a given set of variables. We 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 coecients of the `direct' VAR representation. Empirical applications illustrate 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'. We also nd that including the selected variables into VARs typically improves forecasting accuracy at short horizons.

Keywords: Vector autoregression; Variable selection; Directed graphs; Multi-step causal-ity; Forecasting; Impulse response analysis (search for similar items in EconPapers)
JEL-codes: C32 C51 C55 E52 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2018-12-10
New Economics Papers: this item is included in nep-ets and nep-mac
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Related works:
Working Paper: Directed Graph and Variable Selection in Large Vector Autoregressive Models (2019)
Working Paper: Directed Graphs and Variable Selection in Large Vector Autoregressive Models (2017)
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