Long-Run Causal Order: A Preliminary Investigation

37 Pages Posted: 23 May 2018 Last revised: 24 Sep 2019

See all articles by Kevin D. Hoover

Kevin D. Hoover

Duke University - Departments of Economics and Philosophy

Date Written: May 11, 2018


The concept of long-run causal order and its relationship to nonstationarity in time series data is explored using some ideas drawn from the literature on graphical causal modeling. Ordinary variables, which are nonstationary only because they are caused by a distinct nonstationary variable, are are distinguished from fundamental trends, which are nonstationary owing to their own-dynamics. Although systems of equations in which the own dynamics appears stationary may, in principle, generate nonstationary behavior through their interaction, it is argued that such behavior is nongeneric and that typically ordinary variables trend because an (often latent) fundamental trends is among their causes. The possibility of inferring the long-run causal structure almong a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.

Keywords: graphical causal modeling, causal search, cointegrated vector autoregression (CVAR), weak exogeneity, irreducible cointegrating relations

JEL Classification: C32, C51, C18

Suggested Citation

Hoover, Kevin D., Long-Run Causal Order: A Preliminary Investigation (May 11, 2018). Economic Research Initiatives at Duke (ERID) Working Paper, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3177111 or http://dx.doi.org/10.2139/ssrn.3177111

Kevin D. Hoover (Contact Author)

Duke University - Departments of Economics and Philosophy ( email )

213 Social Sciences Building
Box 90097
Durham, NC 27708-0204
United States

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