Sebastiano Manzan (CeNDEF, University of Amsterdam)
Abstract
Information theoretic tests for serial independence and linearity in time series are proposed. The conditional mutual information is used as a test statistic and estimated nonparametrically using the correlation integral from chaos theory. An advantage of this approach is, that in case of rejection of the null hypothesis, information about the order in which the dependence is present is readily available. The significance of the test statistic is determined by means of bootstrap methods. Size and power of the test are studied using simulated time series using both linear and nonlinear models, with an emphasis on econometrically relevant models. Finally, application to macroeconomic data show evidence of nonlinear dependence.
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Publisher Info
Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Workshop Papers, January 2001 with number
3A.4.
Length: Date of creation: 04 Jan 2001 Date of revision: Handle: RePEc:ams:cdws01:3a.4
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