Tests for Serial Independence and Linearity based on Correlation Integrals
AbstractWe propose information theoretic tests for serial independence and linearity in time series. The test statisticsare based on the conditional mutual information, a general measure of dependence between lagged variables. In caseof rejecting the null hypothesis, this readily provides insights into the lags through which the dependence arises.The conditional mutual information is estimated using the correlation integral from chaos theory. The signi[tanceof the test statistics is determined with a permutation procedure and a parametric bootstrap in the testsfor serial independence and linearity, respectively.The size and power properties of the tests are examined numerically and illustrated with applications to somebenchmark time series.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-085/1.
Date of creation: 12 Sep 2001
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serial independence; linearity; bootstrap; permutation test; nonparametric estimation; nonlinear time series analysis; correlation integral;
Other versions of this item:
- Diks Cees & Manzan Sebastiano, 2002. "Tests for Serial Independence and Linearity Based on Correlation Integrals," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(2), pages 1-22, July.
- Diks, C.G.H. & Manzan, S., 2001. "Tests for serial independence and linearity based on correlation integrals," CeNDEF Working Papers 01-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-16 (All new papers)
- NEP-ECM-2001-10-16 (Econometrics)
- NEP-ETS-2001-10-16 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Diks, C.G.H., 1999. "Consistent Testing for Serial Independence," CeNDEF Working Papers 99-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- repec:att:wimass:9520 is not listed on IDEAS
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"Information-Theoretic Analysis of Serial Dependence and Cointegration,"
Studies in Nonlinear Dynamics & Econometrics,
De Gruyter, vol. 3(3), pages 1-24, October.
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- Robinson, P M, 1991. "Consistent Nonparametric Entropy-Based Testing," Review of Economic Studies, Wiley Blackwell, vol. 58(3), pages 437-53, May.
- Diks Cees & Panchenko Valentyn, 2008.
"Rank-based Entropy Tests for Serial Independence,"
Studies in Nonlinear Dynamics & Econometrics,
De Gruyter, vol. 12(1), pages 1-21, March.
- Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
- Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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