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Finite-sample Resampling-based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability

机译:基于有限样本重采样的组合假设检验,及其在序列相关性和可预测性上的应用

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This article suggests Monte Carlo multiple test procedures which are provably valid in finite samples. These include combination methods originally proposed for independent statistics and further improvements which formalize statistical practice. We also adopt the Monte Carlo test method to noncontinuous combined statistics. The methods suggested are applied to test serial dependence and predictability. In particular, we introduce and analyze new procedures that account for endogenous lag selection. A simulation study illustrates the properties of the proposed methods. Results show that concrete and nonspurious power gains (over standard combination methods) can be achieved through the combined Monte Carlo test approach, and confirm arguments in favor of variance-ratio type criteria.
机译:本文提出了蒙特卡洛多重测试程序,这些程序在有限样本中被证明是有效的。这些包括最初为独立统计建议的组合方法,以及使统计实践正式化的进一步改进。我们还对非连续组合统计采用蒙特卡洛检验方法。建议的方法适用于测试序列依赖性和可预测性。特别是,我们介绍和分析了解释内生滞后选择的新程序。仿真研究说明了所提出方法的性质。结果表明,通过组合的蒙特卡洛检验方法可以实现具体的和非虚假的功率增益(通过标准组合方法),并确认支持方差比类型标准的论点。

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