Matthew D. Webb
Statistics and Probability · 2013–2022
A researcher in the Science corpus: 6 papers; trusts 49 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 81 citations into it; #2,638 of 12,932 by trust here, #2,579 by citations
- Economics and Econometrics — 46 citations into it; #5,622 of 23,481 by trust here, #8,485 by citations
- Artificial Intelligence — 12 citations into it; #22,920 of 35,345 by trust here, #29,412 by citations
- Sociology and Political Science — 6 citations into it
- Finance — 5 citations into it
Whom Matthew D. Webb trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- James G. MacKinnon
- Colin Cameron
- Russell Davidson
- S. B. Thompson
- Rustam Ibragimov
- Timothy G. Conley
- Joshua D. Angrist
- Alberto Abadie
- Guido W. Imbens
- Christian Hansen
Who trusts Matthew D. Webb overall
- James G. MacKinnon
- F. Pretis
- Morten Ørregaard Nielsen
- Antoine A. Djogbenou
- Elizabeth Tipton
- E. Oczkowski
- D. Roodman
- M. Brewer
- James Pustejovsky
- Hristos Doucouliagos
Papers from Matthew D. Webb's perspective
Ordered by how strongly the researchers Matthew D. Webb trusts endorse each paper. Same corpus, a different order for every researcher.
- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- How Much Should We Trust Differences-in-Differences Estimates?
- Asymptotic properties of a robust variance matrix estimator for panel data when T is large
- Mostly Harmless Econometrics: An Empiricist's Companion
- Longitudinal data analysis using generalized linear models
- Robust Inference With Multiway Clustering
- Inference with dependent data using cluster covariance estimators
- Inference with Difference-in-Differences and Other Panel Data
- t-Statistic Based Correlation and Heterogeneity Robust Inference
- Asymptotic Behavior of a t-Test Robust to Cluster Heterogeneity (Review of Economics and Statistics)
Their papers
- Cluster-robust inference: A guide to empirical practice (2022) — 281 citations
- Wild Bootstrap Inference for Wildly Different Cluster Sizes (2013) — 411 citations
- The Wild Bootstrap For Few (treated) Clusters (2018) — 231 citations
- Fast and wild: Bootstrap inference in Stata using boottest (2018) — 706 citations
- Wild Bootstrap and Asymptotic Inference With Multiway Clustering (2019) — 74 citations
- Randomization inference for difference-in-differences with few treated clusters (2020) — 162 citations