t-Statistic Based Correlation and Heterogeneity Robust Inference
提出一种通用方法，在数据存在未知异质性和相关性时，对感兴趣的标量参数进行稳健推断。通过将数据分成至少两组并分别估计，再对估计量进行标准t检验，可在小样本下保持保守性，适用于时间序列、面板、聚类和空间相关数据。
We develop a general approach to robust inference about a scalar parameter of interest when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t-test: For a significance level of 5% or lower, the t-test remains conservative for underlying observations that are independent and Gaussian with heterogenous variances. One might thus conduct robust large sample inference as follows: partition the data into q≥2 groups, estimate the model for each group, and conduct a standard t-test with the resulting q parameter estimators of interest. This results in valid and in some sense efficient inference when the groups are chosen in a way that ensures the parameter estimators to be asymptotically independent, unbiased and Gaussian of possibly different variances. We provide examples of how to apply this approach to time series, panel, clustered and spatially correlated data.