Matthew D. Webb

Associate Professor

• B.A., M.A. (Wilfrid Laurier), Ph.D. (Queen’s)
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Research fields:  applied econometrics and labour economics

Expertise:
• cluster robust inference
• bootstrap inference
• micro-data analysis

Refereed Publications in the Last 6 Years:

For a Complete List of Publications: Matthew Webb Google Scholar

Most Significant Career Research Contributions:

“Cluster-Robust Interference: A Guide to Empirical Practice,” (with James G. MacKinnon, and Morten Ø. Nielsen), Journal of Econometrics, Volume 232, Issue 2, 2023, Pages 272-299.

“Reworking Wild Bootstrap-Based Inference for Clustered Errors”, Canadian Journal of Economics, Volume 56, Issue 3, 2023, Pages 839-858.

“Fast and Wild: Bootstrap Inference in Stata using Boottest,” (with David Roodman, James G. MacKinnon, and Morten Ø. Nielsen), The Stata Journal, Volume 19, Issue 1, 2019, Pages 4–60.

“Randomization Inference for Difference-in-Differences with Few Treated Clusters,” (with James G. Mackinnon), Journal of Econometrics, Volume 218, Issue 2, October 2020, Pages 435-450. 

“The Wild Bootstrap for Few (Treated) Clusters” (with James G. MacKinnon), Econometrics Journal, Volume 21, Number 2, June 2018, Pages 114-135.

“Wild Bootstrap Inference for Wildly Different Cluster Sizes” (with James G. MacKinnon), Journal of Applied Econometrics, Volume 32, Number 2, March 2017, Pages 233–254.