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  • 标题:Information-Theoretic Distribution Test With Application to Normality
  • 作者:Thanasis Stengos ; Ximing Wu
  • 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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