Jeffrey D. Michler, Anna Josephson
arXiv 20 Jul 2021 · Econometrics · 4 citations (OpenAlex)
arXiv:2107.09736 · PDF · DOI · OpenAlex · Extracted main text
We provide a review of recent developments in the calculation of standard errors and test statistics for statistical inference. While much of the focus of the last two decades in economics has been on generating unbiased coefficients, recent years has seen a variety of advancements in correcting for non-standard standard errors. We synthesize these recent advances in addressing challenges to conventional inference, like heteroskedasticity, clustering, serial correlation, and testing multiple hypotheses. We also discuss recent advancements in numerical methods, such as the bootstrap, wild bootstrap, and randomization inference. We make three specific recommendations. First, applied economists need to clearly articulate the challenges to statistical inference that are present in data as well as the source of those challenges. Second, modern computing power and statistical software means that applied economists have no excuse for not correctly calculating their standard errors and test statistics. Third, because complicated sampling strategies and research designs make it difficult to work out the correct formula for standard errors and test statistics, we believe that in the applied economics profession it should become standard practice to rely on asymptotic refinements to the distribution of an estimator or test statistic via bootstrapping. Throughout, we reference built-in and user-written Stata commands that allow one to quickly calculate accurate standard errors and relevant test statistics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Angrist, J. and J.-S. Pischke (2009) Mostly Harmless Econometrics: An Empiricist’s Companion | 1.000 | 15 | 4 | 100% |
| 2 | Abadie, A., S. Athey, G. W. Imbens, and J. M. Wooldridge (2020) Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis | 1.000 | 8 | 3 | 100% |
| 3 | Abadie, A., S. Athey, G. Imbens, and J. Wooldridge (2017) When Should You Adjust Standard Errors for Clustering? | 0.874 | 6 | 2 | 100% |
| 4 | Imbens, G. W. and D. B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences | 0.843 | 3 | 3 | 100% |
| 5 | Deaton, A (2010) Instruments, Randomization, and Learning about Development | 0.737 | 3 | 2 | 100% |
| 6 | MacKinnon, J. G. and H. White (1985) Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties | 0.693 | 7 | 1 | 100% |
| 7 | Imbens, G. W. and M. Kolesár (2016) Robust Standard Errors in Small Samples: Some Practical Advice | 0.693 | 6 | 1 | 100% |
| 8 | Young, A (2016) Improved, Nearly Exact, Statistical Inference with Robust and Clustered Covariance Matrices using Effective Degrees of Freedom C… | 0.693 | 6 | 1 | 100% |
| 9 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How Much Should We Trust Differences-in-Differences Estimates? | 0.644 | 4 | 1 | 100% |
| 10 | Cameron, A. C. and D. L. Miller (2015) A Practitioner’s Guide to Cluster-Robust Inference | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 83 scored citations.