Bootstrap Tests of Nonnested Hypotheses: Some Further Results
研究了非嵌套模型检验中参考分布检验的渐近有效性，并在线性和对数回归模型下通过蒙特卡洛模拟比较了多种检验的有限样本性质，对使用Bootstrap临界值的学者有参考价值。
Abstract Nonnested models are sometimes tested using a simulated reference distribution for the uncentred log likelihood ratio statistic. This approach has been recommended for the specific problem of testing linear and logarithmic regression models. The general asymptotic validity of the reference distribution test under correct choice of error distributions is questioned. The asymptotic behaviour of the test under incorrect assumptions about error distributions is also examined. In order to complement these analyses, Monte Carlo results for the case of linear and logarithmic regression models are provided. The finite sample properties of several standard tests for testing these alternative functional forms are also studied, under normal and nonnormal error distributions. These regression-based variable-addition tests are implemented using asymptotic and bootstrap critical values.