 Testing for co-nonlinearityStudies in Nonlinear Dynamics & Econometrics, 2015, vol. 19, issue 3, 339-353 Abstract: This article introduces the concept of co-nonlinearity. Co-nonlinearity is an example of a common feature in time series [Engle, Robert F., and Sharon Kozicki. 1993. “Testing for Common Features.” Journal of Business & Economic Statistics 11 (4): 369–380] and an extension of the concept of common nonlinear components [Anderson, Heather M., and Farshid Vahid. 1998. “Testing Multiple Equation Systems for Common Nonlinear Components.” Journal of Econometrics 84 (1): 1–36]. If some time series follow a nonlinear process but where a linear relationship between the levels of these series removes the nonlinearity, such a relationship is defined as co-nonlinear. In this article I show how to determine the number of such co-nonlinear relationships. Furthermore, I show how to formulate hypothesis tests on the co-nonlinear relationships in a full maximum likelihood framework. The framework for identifying co-nonlinear relationships is illustrated in a system of Norwegian interest rates. Keywords: common features; nonlinearity; reduced-rank regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:19:y:2015:i:3:p:339-353:n:2 Ordering information: This journal article can be ordered from Access Statistics for this article Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter |
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