Inference with Few Heterogeneous Clusters

By Rustam Ibragimov, Ulrich K. Müller. Review of Economics and Statistics, 2016

109 citations recorded by the source corpus.

Subfields: Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c59d043a98e757d8b5eacabbe627a8dbff283cdb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Statistics and Probability

Citations in the corpus

Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models (2016, background): “Ibragimov and Müller (2010; 2016) proposed cluster-robust t-tests that maintain the nominal Type I error rate by reweighting withincluster estimators of the target parameter.”
Cluster-robust inference: A guide to empirical practice (2022, methodology): “Imbens and Kolesár (2016) proposes a similar procedure for t-tests based on CV2 under the assumption that the variance matrix of u corresponds to a random-effects model.”
Asymptotic theory for clustered samples (2019, unknown): “Lemma 1 implies that ∥∥n−1 g f̃g ∥∥ 2 is uniformly integrable given Assumption (21).”
Wild Bootstrap and Asymptotic Inference With Multiway Clustering (2019, background): “More recent articles include Imbens and Kolesár (2016), Ibragimov and Müller (2016), MacKinnon and Webb (2017, 2018), Carter, Schnepel, and Steigerwald (2017), Pustejovsky and Tipton (2018), and Djogbenou, MacKinnon, and Nielsen (2019).”
The Wild Bootstrap with a “Small” Number of “Large” Clusters (2019, background): “It follows that when these conditions are implausible and there are few clusters, researchers may wish to consider methods that do not impose such restrictions, such as Ibragimov and Müller (2010) and Canay et al.”
What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, unknown): “For instance, Canay et al. (2017), Ibragimov and Müller (2016), Hagemann (2021), and Chernozhukov et al. (2021) respectively propose permutation-based, t -test based, adjusted permutation-based, and conformal inference-based procedures that allow one to relax distributional assumptions about common…”