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Tests of Independence in Separable Econometric Models: Theory and Application

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Author Info

  • Donald J. Brown

    ()
    (Economic Growth Center, Yale University)

  • Rahul Deb

    ()
    (Yale University)

  • Marten H. Wegkamp

    ()
    (Florida State University)

Abstract

A common stochastic restriction in econometric models separable in the latent variables is the assumption of stochastic independence between the unobserved and observed exogenous variables. Both simple and composite tests of this assumption are derived from properties of independence empirical processes and the consistency of these tests is established. As an application, we stimulate estimation of a random quasilinear utility function, where we apply our tests of independence.

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File URL: http://www.econ.yale.edu/growth_pdf/cdp946.pdf
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Bibliographic Info

Paper provided by Economic Growth Center, Yale University in its series Working Papers with number 946.

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Length: 26 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:egc:wpaper:946

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Keywords: Cramér-von Mises distance; empirical independence processes; random utility models; semiparametric econometric models; specification test of independence;

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References

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  1. Donald J. Brown & Caterina Calsamiglia, 2005. "The Nonparametric Approach to Applied Welfare Analysis," Cowles Foundation Discussion Papers 1507, Cowles Foundation for Research in Economics, Yale University.
  2. repec:cup:cbooks:9780521496032 is not listed on IDEAS
  3. Rosa L. Matzkin, 2008. "Identification in Nonparametric Simultaneous Equations Models," Econometrica, Econometric Society, vol. 76(5), pages 945-978, 09.
  4. Roehrig, Charles S, 1988. "Conditions for Identification in Nonparametric and Parametic Models," Econometrica, Econometric Society, vol. 56(2), pages 433-47, March.
  5. Brown, Bryan W, 1983. "The Identification Problem in Systems Nonlinear in the Variables," Econometrica, Econometric Society, vol. 51(1), pages 175-96, January.
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Cited by:
  1. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.

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