This paper provides a simple technique of carrying out inference robust to serial correlation, heteroskedasticity and spatial correlation on the estimators which follow an asymptotic normal distribution. The idea is based on the fact that the estimates from a larger sample tend to have a smaller variance which can be expressed as a function of the variance of the estimator from smaller subsamples. The major advantage of the technique other than the ease of application and simplicity is its finite sample performance both in terms of the empirical null rejection probability as well as the power of the test. It does not restrict the data in terms of structure in any way and works pretty well for any kind of heteroskedasticity, autocorrelation and spatial correlation in a finite sample. Furthermore, unlike theoretical HAC robust techniques available in the existing literature, it does not require any kernel estimation and hence eliminates the discretion of the analyst to choose a specific kernel and bandwidth. The technique outperforms the Ibragimov and Müller ( 2010 ) approach in terms of null rejection probability as well as the local asymptotic power of the test.

中文翻译：

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子采样的可靠推断

本文提供了一种简单技术，可以对遵循渐近正态分布的估计量进行对序列相关性，异方差性和空间相关性的鲁棒性推断。该思想基于以下事实：来自较大样本的估计往往具有较小的方差，该方差可以表示为来自较小子样本的估计量方差的函数。除易用性和简便性外，该技术的主要优势在于，在经验上的无效剔除概率以及测试能力方面，其有限的样本性能。它不以任何方式限制数据的结构，并且对于有限样本中的任何种类的异方差，自相关和空间相关都非常有效。此外，与现有文献中可用的理论HAC鲁棒技术不同，它不需要任何内核估计，因此消除了分析人员选择特定内核和带宽的酌处权。该技术优于Ibragimov和Müller（ 2010年 ）方法的无效拒绝率以及测试的局部渐近能力。