A Simple Parametric Model Selection Test
A Simple Parametric Model Selection Test
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简单的参数模型选择测试
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Wilhelm
中科院分区：
文献类型：
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作者：
Susanne M. Schennach;D. Wilhelm
我们提出了一种简单的模型选择检验，用于在两个参数似然之间进行选择，它可以应用于最一般的情况，而不需要对候选模型与真实分布之间的关系做任何假设。也就是说，允许正确指定或错误指定两者之一或两者都不允许，它们可以是嵌套的、非嵌套的、严格非嵌套的或重叠的。与以前的测试方法不同，不需要预先测试，因为在每种情况下，都可以使用相同的测试统计数据和标准的正常临界值。新程序在一大类数据生成过程中均匀地控制渐近大小。我们在蒙特卡罗实验中展示了它的有限样本性质，并在比较凯恩斯主义和新古典宏观经济模型的实证应用中展示了它的实际意义。这篇文章的补充材料可以在网上找到。
ABSTRACT We propose a simple model selection test for choosing among two parametric likelihoods, which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly specified or misspecified, they may be nested, nonnested, strictly nonnested, or overlapping. Unlike in previous testing approaches, no pretesting is needed, since in each case, the same test statistic together with a standard normal critical value can be used. The new procedure controls asymptotic size uniformly over a large class of data-generating processes. We demonstrate its finite sample properties in a Monte Carlo experiment and its practical relevance in an empirical application comparing Keynesian versus new classical macroeconomic models. Supplementary materials for this article are available online.