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Testing the Normality Assumption in the Tobit Model

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This paper examines a number of statistics that have been proposed to test the normality assumption in the tobit (censored regression) model. It argues that a number of commonly proposed statistics can be interpreted as different versions of the Lagrange multiplier, or score, test for a common null hypothesis. This observation is useful in examining the Monte Carlo results presented in the paper. The Monte Carlo results suggest that the computational convenience of a number of statistics is obtained at the cost of poor finite sample performance under the null hypothesis.
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Keywords: Tobit (Censored Regression) and Probit models; hours of work equations; language multiplier (score) tests; normality

Document Type: Research Article

Affiliations: Department of Economics, University of Strathclyde, Glasgow, UK

Publication date: June 1, 2004

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