IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v29y2010i3p276-306.html
   My bibliography  Save this article

Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes

Author

Listed:
  • Marcelo Fernandes
  • Breno Neri

Abstract

This article develops nonparametric tests of independence between two stochastic processes satisfying β-mixing conditions. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, we take advantage of a generalized entropic measure so as to build a whole family of nonparametric tests of independence. We derive asymptotic normality and local power using the functional delta method for kernels. As a corollary, we also develop a class of entropy-based tests for serial independence. The latter are nuisance parameter free, and hence also qualify for dynamic misspecification analyses. We then investigate the finite-sample properties of our serial independence tests through Monte Carlo simulations. They perform quite well, entailing more power against some nonlinear AR alternatives than two popular nonparametric serial-independence tests.

Suggested Citation

  • Marcelo Fernandes & Breno Neri, 2010. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 276-306.
  • Handle: RePEc:taf:emetrv:v:29:y:2010:i:3:p:276-306
    DOI: 10.1080/07474930903451557
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930903451557
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Zheng, John Xu, 2000. "A Consistent Test Of Conditional Parametric Distributions," Econometric Theory, Cambridge University Press, vol. 16(05), pages 667-691, October.
    2. Drost, F.C. & Werker, B.J.M., 1993. "A Note on Robinson's Test of Independence," Papers 9315, Tilburg - Center for Economic Research.
    3. Gregory, Allan W & Sampson, Michael J, 1991. "Testing Long-Run Properties of Stationary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 287-295, July.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Baeck, E.G. & Brock, W.A., 1992. "A Nonparametric Test for Independence of a Multivariate Time Series," Working papers 9204, Wisconsin Madison - Social Systems.
    6. Marc Hallin & Jana Jureckova & Jan Picek & Toufik Zahaf, 1999. "Nonparametric tests of independence between two autoregressive series based on autoregression rank scores," ULB Institutional Repository 2013/2081, ULB -- Universite Libre de Bruxelles.
    7. Jiti Gao & Maxwell King, 2004. "Model Specification Testing in Nonparametric and Semiparametric Time Series Econometric Models," Econometric Society 2004 North American Winter Meetings 225, Econometric Society.
    8. P. M. Robinson, 1991. "Consistent Nonparametric Entropy-Based Testing," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 437-453.
    9. Marc Hallin & Madan Lal Puri, 1992. "Rank tests for time-series analysis: a survey," ULB Institutional Repository 2013/2229, ULB -- Universite Libre de Bruxelles.
    10. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    11. Bruce Mizrach, 1995. "A Simple Nonparametric Test for Independence," Departmental Working Papers 199523, Rutgers University, Department of Economics.
    12. Phillips, P C B, 1991. "Error Correction and Long-Run Equilibrium in Continuous Time," Econometrica, Econometric Society, vol. 59(4), pages 967-980, July.
    13. Yongmiao Hong & Halbert White, 2005. "Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence," Econometrica, Econometric Society, vol. 73(3), pages 837-901, May.
    14. Yanqin Fan & Oliver Linton, 1997. "Some Higher Order Theory for a Consistent Nonparametric Model Specification Test," Cowles Foundation Discussion Papers 1148, Cowles Foundation for Research in Economics, Yale University.
    15. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    2. Paulo Ferreira & Andreia Dionísio, 2014. "Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece," Applied Financial Economics, Taylor & Francis Journals, vol. 24(5), pages 319-331, March.
    3. Atanu Biswas & Maria Carmen Pardo & Apratim Guha, 2014. "Auto-association measures for stationary time series of categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 487-514, September.
    4. George Kapetanios, 2007. "A Test for Serial Dependence Using Neural Networks," Working Papers 609, Queen Mary University of London, School of Economics and Finance.
    5. Simone Giannerini & Eefandiar Maasoumi & Estela Bee Dagum, 2013. "Entropy Testing for Nonlinearity in Time Series," Emory Economics 1307, Department of Economics, Emory University (Atlanta).
    6. repec:eee:phsmap:v:486:y:2017:i:c:p:730-750 is not listed on IDEAS
    7. Ferreira, Paulo & Dionísio, Andreia & Movahed, S.M.S., 2017. "Assessment of 48 Stock markets using adaptive multifractal approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 730-750.
    8. Paulo Ferreira, 2012. "Testing serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece," CEFAGE-UE Working Papers 2012_24, University of Evora, CEFAGE-UE (Portugal).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:emetrv:v:29:y:2010:i:3:p:276-306. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://www.tandfonline.com/LECR20 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.