摘要翻译：
本文分析了用bootstrap方法检验两阶段最小二乘法估计的线性模型参数变化的方法。考虑了两种类型的检验：一种是零假设不变，替代假设在样本中k个未知断点处有离散变化；第二个检验，其中零假设是在样本中的l个断点处有离散的参数变化，而另一个选择是在l+1个断点处参数变化。在这两种情况下，我们使用野生递归引导或野生固定引导来考虑基于sup-wald型统计量的推理。我们在一组允许误差呈现条件和/或无条件异方差的一般条件下建立了这些bootstrap检验的渐近有效性，并报告了一项模拟研究的结果，表明这些检验在宏观经济学中经常遇到的样本量中产生了可靠的推论。分析了2SLS的第一阶段估计涉及一个参数为常数或其本身受离散参数变化影响的模型的情况。如果误差表现出无条件异方差性和/或简化形式不稳定，那么引导方法是特别有吸引力的，因为测试统计量的极限分布不是关键的。
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英文标题：
《Bootstrapping Structural Change Tests》
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作者：
Otilia Boldea (Tilburg University), Adriana Cornea-Madeira (University
  of York) and Alastair R. Hall (University of Manchester)
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper analyses the use of bootstrap methods to test for parameter change in linear models estimated via Two Stage Least Squares (2SLS). Two types of test are considered: one where the null hypothesis is of no change and the alternative hypothesis involves discrete change at k unknown break-points in the sample; and a second test where the null hypothesis is that there is discrete parameter change at l break-points in the sample against an alternative in which the parameters change at l + 1 break-points. In both cases, we consider inferences based on a sup-Wald-type statistic using either the wild recursive bootstrap or the wild fixed bootstrap. We establish the asymptotic validity of these bootstrap tests under a set of general conditions that allow the errors to exhibit conditional and/or unconditional heteroskedasticity, and report results from a simulation study that indicate the tests yield reliable inferences in the sample sizes often encountered in macroeconomics. The analysis covers the cases where the first-stage estimation of 2SLS involves a model whose parameters are either constant or themselves subject to discrete parameter change. If the errors exhibit unconditional heteroskedasticity and/or the reduced form is unstable then the bootstrap methods are particularly attractive because the limiting distributions of the test statistics are not pivotal.
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PDF链接：
https://arxiv.org/pdf/1811.04125