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Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study*

2002; Wiley; Volume: 64; Issue: 4 Linguagem: Inglês

10.1111/1468-0084.00025

1468-0084

Alfred A. Haug,

Fiscal Policies and Political Economy

Oxford Bulletin of Economics and StatisticsVolume 64, Issue 4 p. 399-412 Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study* Alfred A. Haug, Alfred A. Haug Department of Economics, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3. E-mail: haug@econ.yorku.caSearch for more papers by this author Alfred A. Haug, Alfred A. Haug Department of Economics, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3. E-mail: haug@econ.yorku.caSearch for more papers by this author First published: 16 October 2002 https://doi.org/10.1111/1468-0084.00025Citations: 170 1 See, for example, Murray and Nelson (2000) for a recent analysis of U.S. GDP behaviour. 2 See also Granger (1990). 3 Earlier Monte Carlo studies on the relation between the span and the frequency of observation by Hakkio and Rush (1991) and by Hooker (1993) analyzed a very restrictive DGP and only one or two single-equation based tests for cointegration. 4 An alternative to temporal aggregation (i.e., summing or averaging the monthly values to generate a quarterly or an annual value) is systematic sampling. Systematic sampling, for example, would use the last month of a quarter to represent a quarterly value. I deal here only with time–aggregated data and do not consider systematic sampling, which has different implications. 5 The Q-statistic has a P-value of .02 at lag 20. 6 Marcellino also discussed the effects of temporal aggregation on exogeneity, causality, the order of integration and cointegration, common cycles, and impulse response functions. 7 See Reimers (1993). Also, Johansen and Juselius suggested in addition a closely related “trace” test, however, it has generally less power in finite samples than the maximum eigenvalue test. 8 See also Gonzalo (1994). 9 See for example Teräsvirta (1998) on various possibilities for nonlinear modelling. * The author thanks, without implicating, the associate editor Anindya Banerjee, anonymous referees, Neil Ericsson, and participants at seminars at Nuffield College and the University of Canterbury for helpful comments and discussions on earlier versions of this paper. Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume64, Issue4September 2002Pages 399-412 RelatedInformation