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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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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

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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    References listed on IDEAS

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    1. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    2. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Bauer, Thomas K. & Million, Andreas & Rotte, Ralph & Zimmermann, Klaus F., 1998. "Immigration Labor and Workplace Safety," IZA Discussion Papers 16, Institute for the Study of Labor (IZA).
    7. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    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. 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.

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    Keywords

    econometrics ; economic models ; tests;

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