Testing Dependence Among Serially Correlated Multi-category Variables

Mohammad Pesaran and Allan Timmermann

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: The contingency table literature on tests for dependence among discrete multi-category variables assume that draws are independent, and there are no tests that account for serial dependencies ? a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods.

Keywords: Contingency Tables; Canonical Correlations; Serial Dependence; Tests of Predictability (search for similar items in EconPapers)
JEL-codes: C12 C22 C42 C52 (search for similar items in EconPapers)
Pages: 33
Date: 2006-07
New Economics Papers: this item is included in nep-ets
Note: Ec
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0648.pdf (application/pdf)

Related works:
Journal Article: Testing Dependence Among Serially Correlated Multicategory Variables (2009)
Working Paper: Testing Dependence among Serially Correlated Multi-category Variables (2006)
Working Paper: Testing Dependence among Serially Correlated Multi-Category Variables (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0648

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