期刊名称：Economics Discussion Papers / Department of Economics, College of Management and Economics, University of Guelph
出版年度：2006
卷号：2006
出版社：University of Guelph
摘要：We derive general distribution tests based on the method of Maximum Entropy
density. The proposed tests are derived from maximizing the di®erential entropy sub-
ject to moment constraints. By exploiting the equivalence between the Maximum
Entropy and Maximum Likelihood estimates of the general exponential family, we can
use the conventional Likelihood Ratio, Wald and Lagrange Multiplier testing princi-
ples in the maximum entropy framework. In particular, we use the Lagrange Multiplier
method to derive tests for normality and their asymptotic properties. Monte Carlo evi-
dence suggests that the proposed tests have desirable small sample properties and often
outperform commonly used tests such as the Jarque-Bera test and the Kolmogorov-
Smirnov-Lillie test for normality. We show that the proposed tests can be extended to
tests based on regression residuals and non-iid data in a straightforward manner. We
apply the proposed tests to the residuals from a stochastic production frontier model
and reject the normality hypothesis
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