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Asymptotic Inference in Censored Regression MOdels Revisited

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Author Info
Chihwa Kao () (Center for Policy Research, Maxwell School, Syracuse University)

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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.

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File URL: http://www-cpr.maxwell.syr.edu/cprwps/pdf/wp36.pdf
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Publisher Info
Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 36.

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Length: 14 pages
Date of creation: Feb 2001
Date of revision:
Handle: RePEc:max:cprwps:36

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Find related papers by JEL classification:
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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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. [Downloadable!] (restricted)
  2. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Olsen, Randall J, 1978. "Note on the Uniqueness of the Maximum Likelihood Estimator for the Tobit Model," Econometrica, Econometric Society, vol. 46(5), pages 1211-15, September. [Downloadable!] (restricted)
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