Inference with few heterogeneous clusters

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Liberation Technology: Mobile Phones and Political Mobilization in Africa

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Household Debt Revaluation and the Real Economy: Evidence from a Foreign Currency Debt Crisis

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Potential Unemployment Insurance Duration and Labor Supply: The Individual and Market-Level Response to a Benefit Cut

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Inference with Dependent Data in Accounting and Finance Applications

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Asymptotic Theory for Clustered Samples

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Contagion and tail risk in complex financial networks

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Wild Bootstrap and Asymptotic Inference With Multiway Clustering

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Persuasion: A Case Study of Papal Influences on Fertility-Related Beliefs and Behavior

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Spatial Correlation Robust Inference

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Inference for Clustered Data

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Testing for the appropriate level of clustering in linear regression models

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Sequencing innovation rollout: Learning opportunity versus entry speed

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The Labor Supply Consequences of the Opioid CrisisPreprint

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Financial Crisis, Creditor-Debtor Conflict, and Political ExtremismPreprint

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Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t -Statistic

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Simple and trustworthy cluster-robust GMM inference

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Inference with a Single Treated Cluster

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Inference with a single treated cluster

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On Inference When Using State Corporate Laws for IdentificationPreprint

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Network Cluster‐Robust Inference

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Comparing Stakeholders’ Knowledge and Beliefs About Supporting Students’ Social, Emotional, and Behavioral Health in Schools

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One country, two systems? The heavy-tailedness of Chinese A- and H- share markets

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Comparing Forecasting Performance with Panel DataPreprint

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Robust inference in conditionally heteroskedastic autoregressions

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Military training exercises, pollution, and their consequences for health

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Can Two Forecasts Have the Same Conditional Expected Accuracy?Preprint

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Wild bootstrap inference for instrumental variables regressions with weak and few clusters

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New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence

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Inference with Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-StatisticPreprint

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The Wild Bootstrap with a Small Number of Large Clusters

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On the Implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters

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Clustering Methods for Statistical Inference

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The changing landscape of cyber risk: An empirical analysis of loss severity and tail dynamics

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Policy Analysis Using Multilevel Regression Models with Group Interactive Fixed Effects

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Dependence‐robust inference using resampled statistics

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Benchmarking Water Retail Cost Efficiency in England and Wales

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VALID HETEROSKEDASTICITY ROBUST TESTING

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Cluster-Robust Standard Errors for Linear Regression Models with Many ControlsPreprint

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Corrigendum: Small Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models

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Robust inference on income inequality: t- statistic based approach

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COVID-19: Tail risk and predictive regressions

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Predictability of cryptocurrency returns: evidence from robust tests

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Education and Internal Migration: Evidence From a Child Labor Reform in Spain

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A Modified Randomization Test for the Level of ClusteringPreprint

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Robust Inference on Income Inequality: t-Statistic Based ApproachesPreprint

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Too Busy for School? The Effect of Athletic Participation on AbsenteeismPreprint

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Inference with Modern Difference-in-Differences MethodsPreprint

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A Modified Randomization Test for the Level of Clustering

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Inference on quantile processes with a finite number of clusters

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Equity returns and sentiment

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Sign tests for dependent observations

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Asymptotic Theory for Clustered SamplesPreprint

Hansen, B. E., & Lee, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3099187 · Cited by 2 · see citing works

New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear DependencePreprint

Андреевич, С. А., Pedersen, R. S., & Ibragimov, R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3580916 · Cited by 2 · see citing works

Using Multiple Outcomes to Adjust Standard Errors for Spatial Correlation

DellaVigna, S., Imbens, G. W., Kim, W., & Ritzwoller, D. M. (2025) · National Bureau of Economic Research · doi:10.3386/w33716 · Cited by 2 · see citing works

Some impossibility results for inference with cluster dependence with large clusters

Kojevnikov, D., & Song, K. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.105524 · Cited by 1 · see citing works

Jackknife Standard Errors for Clustered Regression

Hansen, B. E. (2026) · The Review of Economic Studies · doi:10.1093/restud/rdag050 · Cited by 1 · see citing works

Assumption-light and computationally cheap inference on inequality measures by sample splitting: the Student t approach

Midões, C., & de Crombrugghe, D. (2023) · The Journal of Economic Inequality · doi:10.1007/s10888-023-09574-w · Cited by 1 · see citing works

Panel Data Inference with Dependent Clusters

Powell, D. (2017) · Cited by 1 · see citing works

Inference and Empirical Examples

Ibragimov, M., Ibragimov, R., & Waldén, J. (2015) · Lecture notes in statistics · doi:10.1007/978-3-319-16877-7_3

Fixed Smoothing Asymptotic Theory in Over-identified Econometric Models in the Presence of Time-series and Clustered Dependence.

Hwang, J. (2016) · eScholarship (California Digital Library)

Dependence-Robust Inference Using Randomized SubsamplingPreprint

Leung, M. P. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3036626

Robust Inference on Income Inequality: \textit{t-}Statistic Based ApproachesPreprint

Ibragimov, R., Kattuman, P. A., & Андреевич, С. А. (2021) · arXiv (Cornell University)

Panel Data with Unknown ClustersPreprint

Cai, Y. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2106.05503

Toward a cross-level understanding of institutional change: a microfoundational perspective

Kuijpers, J. C. (2021) · doi:10.3990/1.9789036551434

Inference with Correlated ClustersPreprint

Powell, D. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2786125

Unbiased estimation of the OLS covariance matrix when the errors are clustered

Boot, T., Niccodemi, G., & Wansbeek, T. J. (2023) · Empirical Economics · doi:10.1007/s00181-023-02379-w

A Market Crash or Tail Risk? Heavy Tails and Asymmetry of Returns in the Chinese Stock Market

Xing, Z., & Ibragimov, R. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/s0731-90532023000045b009

The Changing Landscape of Cyber Risk: An Empirical Analysis of Frequency, Severity, and Tail DynamicsPreprint

Eling, M., Ibragimov, R., & Ning, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4827872

Unbiased estimation of the OLS covariance matrix when the errors are clustered

Boot, T., Niccodemi, G., & Wansbeek, T. J. (2023) · Advanced studies in theoretical and applied econometrics · doi:10.1007/978-3-031-48385-1_3

Using Multiple Outcomes to Adjust Standard Errors for Spatial CorrelationPreprint

DellaVigna, S., Imbens, G. W., Kim, W., & Ritzwoller, D. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5233074

Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters

Wang, W., & Zhang, Y. (2026) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2026.2618311

All that legislates is not gold: Representation and redistribution in Chile

Contreras, C. M., & Corvalán, A. (2026) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2026.102818

Statistical Comparison of Forecasts Made at Different Frequencies

Zhang, R., Zu, Y., & Li, W. (2026) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2026.2634825

A comparison of forecasting performance of some panel of various commodities prices and time-series models

Drachal, K., & Tabash, M. I. (2026) · International May Conference on Strategic Management (IMCSM) · doi:10.5937/imcsm26407d

Tail risk, large fluctuations and downfalls in renewable energy markets

Hasanov, A. S., Ibragimov, R., Yıldırım, R., Chong, S. K., & Abrorov, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351106

Inference with Few Heterogenous Clusters

Ibragimov, R., & Müller, U. K. (2013)

Cluster-robust inference with a single treated cluster using the t-testPreprint

Lau, C. P., & Li, X. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2511.05710

Inference with a Single Treated Cluster

Hagemann, A. (2025) · The Review of Economic Studies · doi:10.1093/restud/rdaf002 · Cited by 19 · see citing works

Permutation Inference with a Finite Number of Heterogeneous Clusters

Hagemann, A. (2023) · The Review of Economics and Statistics · doi:10.1162/rest_a_01300 · Cited by 9 · see citing works

t -Statistic Based Correlation and Heterogeneity Robust Inference

Ibragimov, R., & Müller, U. K. (2009) · Journal of Business and Economic Statistics · doi:10.1198/jbes.2009.08046 · Cited by 300 · see citing works