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Tests of Independence in Parametric Models : with Applications and Illustrations

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  • Cameron, C.
  • Trivedi, P.K.

Abstract

Tests of independence between variables in discrete and continuous bivariate and multivariate regression equations are derived using series expansions of joint distributions in terms of marginal distributions and their related orthonormal polynomials. Th e tests are conditional moment tests based on covariances between pair s of orthonormal polynomials. Examples include tests of serial independence against bilinear and/or autoregressive conditional heteroskedasticity alternatives, dependence in multivariate normal regression models, and dependence in count data models. Monte Carlo simulations based on bivariate counts are used to evaluate the tests. A multivariate count data model for Australian health-care utilization data is used for illustration.
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Suggested Citation

  • Cameron, C. & Trivedi, P.K., 1992. "Tests of Independence in Parametric Models : with Applications and Illustrations," Papers 9237, Tilburg - Center for Economic Research.
    • Handle: RePEc:fth:tilbur:9237
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    Cited by:

    1. Godfrey, Leslie G., 1996. "Some results on the Glejser and Koenker tests for heteroskedasticity," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 275-299.
    2. Sullivan Hu'e & Christophe Hurlin & Yang Lu, 2024. "Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials," Papers 2405.02012, arXiv.org, revised May 2024.
    3. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions," Journal of Econometrics, Elsevier, vol. 106(1), pages 143-170, January.
    4. Fichera, Eleonora & Emsley, Richard & Sutton, Matt, 2016. "Is treatment “intensity” associated with healthier lifestyle choices? An application of the dose response function," Economics & Human Biology, Elsevier, vol. 23(C), pages 149-163.
    5. Matilla-Garcia, Mariano, 2007. "A non-parametric test for independence based on symbolic dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 31(12), pages 3889-3903, December.
    6. Bauer, Thomas K. & Million, Andreas & Rotte, Ralph & Zimmermann, Klaus F., 1998. "Immigration Labor and Workplace Safety," IZA Discussion Papers 16, Institute of Labor Economics (IZA).
    7. Paulos Teckle & Matt Sutton, 2008. "How Do the Determinants of Demand for GP Visits Respond to Higher Supply? An Analysis of Grouped Counts," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(III), pages 495-513, September.
    8. Begoña Álvarez & Daniel Miles, 2003. "Gender effect on housework allocation: Evidence from Spanish two-earner couples," Journal of Population Economics, Springer;European Society for Population Economics, vol. 16(2), pages 227-242, May.
    9. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    10. A. Colin Cameron & Per Johansson, 2004. "Bivariate Count Data Regression Using Series Expansions: With Applications," Working Papers 9815, University of California, Davis, Department of Economics.
    11. Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
    12. Charles G. Renfro, 2009. "The Practice of Econometric Theory," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75571-5.
    13. Emilio Gómez-Déniz & Jorge Pérez-Rodríguez, 2015. "Closed-form solution for a bivariate distribution in stochastic frontier models with dependent errors," Journal of Productivity Analysis, Springer, vol. 43(2), pages 215-223, April.
    14. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.

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