Directed Graph and Variable Selection in Large Vector Autoregressive Models

Dominik Bertsche, Ralf Brüggemann and Christian Kascha ()

VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy from Verein für Socialpolitik / German Economic Association

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 coefficients 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 find that including the selected variables into VARs typically improves forecasting accuracy at short horizons.

Keywords: Vector autoregression; Variable selection; Directed graphs; Multi-step causality; Forecasting; Impulse response analysis (search for similar items in EconPapers)
JEL-codes: C32 C51 C55 E52 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-mac and nep-ore
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Related works:
Working Paper: Directed Graphs and Variable Selection in Large Vector Autoregressive Models (2018)
Working Paper: Directed Graphs and Variable Selection in Large Vector Autoregressive Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc19:203656

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