Post-Sample Granger Causality Analysis: A New (Relatively) Large-Scale Exemplar

38 Pages Posted: 7 Jan 2014

See all articles by Haichun Ye

Haichun Ye

Shanghai University of Finance and Economics - School of Economics

Richard A. Ashley

Virginia Tech. - Department of Economics

John Guerard

McKinley Capital Management, LLC

Date Written: January 5, 2014

Abstract

Ashley and Ye (2012) exemplifies the state-of-the-art in post-sample Granger causality analysis in a small-scale (bivariate) setting, albeit with a sufficiently large sample (T = 480 months) as to make post-sample testing feasible. In the present work we extend this work in two directions. First, here we analyze four macroeconomically important endogenous variables – monthly measures of aggregate income, consumption, consumer prices, and the unemployment rate – embedded in a six-dimensional information set which also includes two interest rates, both taken to be exogenous. Second, we compare the causality results obtained using a traditional large-to-small (but partially judgmental) model identification procedure to those obtained using the objective (but mechanical) "Autometrics" identification procedure given by Doornik and Hendry (2007).

Keywords: Granger causality, out-of-sample testing, post-sample testing

JEL Classification: C18, C22, C32, E20, E30

Suggested Citation

Ye, Haichun and Ashley, Richard A. and Guerard, John, Post-Sample Granger Causality Analysis: A New (Relatively) Large-Scale Exemplar (January 5, 2014). Available at SSRN: https://ssrn.com/abstract=2375497 or http://dx.doi.org/10.2139/ssrn.2375497

Haichun Ye (Contact Author)

Shanghai University of Finance and Economics - School of Economics ( email )

111 Wuchuan Road
Shanghai, 200434
China

Richard A. Ashley

Virginia Tech. - Department of Economics ( email )

250 Drillfield Drive
Blacksburg, VA 24061
United States

John Guerard

McKinley Capital Management, LLC ( email )

3301 C St # 500
Anchorage, AK 99503
United States

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