Asymptotic Behavior of a t-Test Robust to Cluster Heterogeneity

By Andrew V. Carter, Kevin T. Schnepel, D. Steigerwald. Review of Economics and Statistics, 2017

135 citations recorded by the source corpus.

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Citations in the corpus

The Wild Bootstrap with a “Small” Number of “Large” Clusters (2019, background): “…analyses of its theoretical properties, which, to the best of our knowledge, all employ an asymptotic framework where the number of clusters tends to infinity—for example, Carter, Schnepel, and Steigerwald (2017), Djogbenou, MacKinnon, and Nielsen (2019), and MacKinnon, Nielsen, and Webb (2019).”
A Practitioner’s Guide to Cluster-Robust Inference (2015, methodology): “In simulations using standard normal critical values, Carter et al. (2013) nd that test size distortion occurs for G < 20.”
Cluster-robust inference: A guide to empirical practice (2022, background): “Carter et al. (2017) suggests setting ρ = 1, as a sort of worst case.”
Asymptotic theory and wild bootstrap inference with clustered errors (2019, background): “, Hansen (2007, Theorem 2) or Carter et al. (2017) for related arguments.”
Fast and wild: Bootstrap inference in Stata using boottest (2018, methodology): “The “effective number of clusters” developed in Carter, Schnepel, and Steigerwald (2017) often provides a useful diagnostic, which can be computed using the package clusteff; see Lee and Steigerwald (2018).”
Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models (2016, methodology): “ber of clusters m, but also on features of the covariates 9 in X. Specically, the bias tends to be larger when the covariates are skewed or unbalanced across clusters, or when clusters vary in size (Carter et al. 2013, MacKinnon 2013). A more principled approach to bias correction would therefore ta”
Asymptotic theory for clustered samples (2019, unknown): “Carter, Schnepel and Steigerwald (2017) provided asymptotic results allowing for heterogeneous clusters, but their results are limited by atypical regularity conditions.”
Wild Bootstrap and Asymptotic Inference With Multiway Clustering (2019, methodology): “Our proof of asymptotic validity builds upon the asymptotic distribution theory with multiway clustering in Davezies et al. (2018) and Menzel (2018).”