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On Limited Dependent Variable Models: Maximum Likelihood Estimation and Test of One-sided Hypothesis

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  • Silvapulle, Mervyn J.

Abstract

The limited dependent variable models with errors having log-concave density functions are studied here. For such models with normal errors, the asymptotic normality of the maximum likelihood estimator was established by Amemiya [1]. We show, when the density of the error distribution is log-concave, that the maximum likelihood estimator exists with arbitrarily large probability for large sample sizes, and is asymptotically normal. The general theory presented here includes the important special cases of normal, logistic, and extreme value error distributions. The main results are established under rather weak conditions. It is also shown that, under the null hypothesis, the asymptotic distribution of the likelihood ratio statistic for testing a one-sided alternative hypothesis is a weighted sum of chi-squares.

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  • Silvapulle, Mervyn J., 1991. "On Limited Dependent Variable Models: Maximum Likelihood Estimation and Test of One-sided Hypothesis," Econometric Theory, Cambridge University Press, vol. 7(3), pages 385-395, September.
  • Handle: RePEc:cup:etheor:v:7:y:1991:i:03:p:385-395_00
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    1. Abadir, Karim M. & Distaso, Walter, 2007. "Testing joint hypotheses when one of the alternatives is one-sided," Journal of Econometrics, Elsevier, vol. 140(2), pages 695-718, October.

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