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Testing for Unobserved Heterogeneity in Exponential and Weibull Duration Models

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  • Jin Seo Cho

    ()
    (Department of Economics, Korea University, Seoul, South Korea)

  • Halbert White

    ()
    (Department of Economics, University of California, San Diego, U.S.A.)

Abstract

We examine use of the likelihood ratio (LR) statistics to test for unobserved heterogeneity in duration models, based on mixtures of exponential or Weibull distributions. We consider both the uncensored and censored duration cases. The asymptotic null distribution of the LR test statistics is not the standard chi-square, as the standard regularity conditions do not hold. Instead, there is a nuisance parameter identified only under the alternative, and a null parameter value on the boundary of parameter space, as in Cho and White (2007a). We accommodate these and provide methods delivering consistent asymptotic critical values. We conduct a number of Monte Carlo simulations, comparing the level and power of the LR test statistics to an information matrix (IM) text due to Chesher (1984) and Lagrange multiplier (LM) tests of Kiefer (1985) and Sharma (1987). Our simulations show that the LR test statistic generally outperforms the IM and LM tests. We aslo revisit the work of van den Berg and Ridder (1998) on unemployment durations and of Ghysels, Gourieroux, and Jasiak (2004)on interarrival times between stock trades, and, as it turns out, affirm their original informal inferences.

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Bibliographic Info

Paper provided by Institute of Economic Research, Korea University in its series Discussion Paper Series with number 0912.

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Length: 63 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:iek:wpaper:0912

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Keywords: Unobserved Heterogeneity; Mixture Models; Likelihood Ratio Test; Search Theory; Interarrival Times;

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References

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  1. Chesher, Andrew D, 1984. "Testing for Neglected Heterogeneity," Econometrica, Econometric Society, vol. 52(4), pages 865-72, July.
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Cited by:
  1. Yae In Baek & Jin Seo Cho & Peter C.B. Phillips, 2013. "Testing Linearity Using Power Transforms of Regressors," Cowles Foundation Discussion Papers 1917, Cowles Foundation for Research in Economics, Yale University.
  2. Kyu Lee Shin & Jin Seo Cho, 2013. "Testing for Neglected Nonlinearity Using Extreme Learning Machines (published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21, Suppl. 2 (2013), 117--129.)," Working papers 2013rwp-57, Yonsei University, Yonsei Economics Research Institute.

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