On Testing the Logistic Assumption in Binary Dependent Variable Models
This paper shows that the LM test for the validity of the logistic distribution commonly assumed in Binary Dependent Variable Models (i.e., the logit model) developed by Poirer (1980) can be obtained from a simple artificial regression. Monte Carlo simulations examine the small sample behavior of the test statistic in comparison to the Information Matrix test of the logit model developed by Orme (1988) and Davidson and MacKinnon (1989), and two versions of the Reset test for limited dependent variable models suggested by Pagan and Vella (1989). Our results suggest that the LM test compares favorably under the null. The tests also appear to have varying power properties against different alternatives which suggests that they should all be used in investigating the validity of the logit model.
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Volume (Year): 18 (1993)
Issue (Month): 2 ()
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