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Asymptotic Inference in Censored Regression MOdels Revisited

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Abstract

This paper establishes that regressors in the models with censored dependent variables need not be bounded for the standard asymptotic results to apply. Thus, regressors that grow monotonically with the observation index may be acceptable. It also purports to provide an upper bound on the rate at which regressors may grow.

Suggested Citation

  • Chihwa Kao, 2001. "Asymptotic Inference in Censored Regression MOdels Revisited," Center for Policy Research Working Papers 36, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:36
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    File URL: https://surface.syr.edu/cpr/119/
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    1. Lai, T. L. & Robbins, Herbert & Wei, C. Z., 1979. "Strong consistency of least squares estimates in multiple regression II," Journal of Multivariate Analysis, Elsevier, vol. 9(3), pages 343-361, September.
    2. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    3. Olsen, Randall J, 1978. "Note on the Uniqueness of the Maximum Likelihood Estimator for the Tobit Model," Econometrica, Econometric Society, vol. 46(5), pages 1211-1215, September.
    4. Gourieroux, Christian & Monfort, Alain, 1981. "Asymptotic properties of the maximum likelihood estimator in dichotomous logit models," Journal of Econometrics, Elsevier, vol. 17(1), pages 83-97, September.
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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