## 22 Citations

### Permutation inference with a finite number of heterogeneous clusters

- Economics
- 2019

I introduce a simple permutation procedure to test conventional (non-sharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when…

### Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters

- Economics, Mathematics
- 2021

A wild bootstrap Anderson-Rubin test for the full-vector inference is developed and it is shown that it controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identiﬁed in at least one of the clusters.

### Robust IV Inference with Clustering Dependence*

- Mathematics
- 2020

Linear IV models with clustering dependence are widely used in empirical studies, although the common solution, the cluster covariance estimator, often produces undesirable inferential results,…

### When and How to Deal with Clustered Errors in Regression Models

- Mathematics, Computer Science
- 2020

We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error…

### Some Impossibility Results for Inference With Cluster Dependence with Large Clusters

- Economics, Computer Science
- 2021

This paper shows that when there is only one large cluster, and the researcher does not have any knowledge on the dependence structure of the observations, it is not possible to consistently discriminate the mean, and provides a necessary and sufﬁcient condition for the cluster structure that the long run variance is consistently estimable.

### Difference-in-Differences for Policy Evaluation

- Economics
- 2022

Diﬀerence-in-diﬀerences is one of the most used identiﬁcation strategies in empirical work in economics. This chapter reviews a number of important, recent developments related to…

### Conducting Research in Marketing with Quasi-Experiments

- BusinessJournal of Marketing
- 2022

This article aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make…

### Inference in Difference‐in‐Differences: How Much Should We Trust in Independent Clusters?

- MathematicsJournal of Applied Econometrics
- 2023

The conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences models are analyzed to provide a better understanding on when spatial correlation should be more problematic, and important guidelines on how to minimize inference problems due to spatial correlation are provided.

## References

SHOWING 1-10 OF 38 REFERENCES

### t-Statistic Based Correlation and Heterogeneity Robust Inference

- Mathematics
- 2007

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…

### On the behaviour of randomization tests without the group invariance assumption

- Mathematics
- 1990

Abstract Fisher's randomization construction of hypothesis tests is a powerful tool to yield tests that are nonparametric in nature in that their level is exactly equal to the nominal level in finite…

### Asymptotic Behavior of a t-Test Robust to Cluster Heterogeneity

- MathematicsReview of Economics and Statistics
- 2017

Abstract For a cluster-robust t-statistic under cluster heterogeneity we establish that the cluster-robust t-statistic has a gaussian asymptotic null distribution and develop the effective number of…

### Inference with Few Heterogeneous Clusters

- MathematicsReview of Economics and Statistics
- 2016

Abstract Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased, and Gaussian parameter estimators. We make two…

### Wild Bootstrap Inference for Wildly Different Cluster Sizes

- Environmental Science
- 2017

Summary
The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced…

### How Much Should We Trust Differences-in-Differences Estimates?

- Mathematics
- 2001

Most Difference-in-Difference (DD) papers rely on many years of data and focus on serially correlated outcomes. Yet almost all these papers ignore the bias in the estimated standard errors that…

### The Subcluster Wild Bootstrap for Few (Treated) Clusters

- Economics
- 2016

Inference based on cluster-robust standard errors is known to fail when the number of clusters is small, and the wild cluster bootstrap fails dramatically when the number of treated clusters is very…

### A Practitioner’s Guide to Cluster-Robust Inference

- Computer ScienceThe Journal of Human Resources
- 2015

This work considers statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters, when the number of clusters is large and default standard errors can greatly overstate estimator precision.

### Studentized permutation tests for non-i.i.d. hypotheses and the generalized Behrens-Fisher problem

- Mathematics
- 1997

### Robust Standard Errors in Small Samples: Some Practical Advice

- MathematicsReview of Economics and Statistics
- 2016

It is shown that standard errors can lead to substantial improvements in coverage rates even for samples with fifty or more clusters, and recommended that researchers routinely calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors.