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(This article was first published on <strong><a href="http://davegiles.blogspot.com/2018/11/more-sandwiches-anyone.html"> Econometrics Beat: Dave Giles' Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<div dir="ltr">Consider this my Good Deed for the Day!
<p>A re-tweet from a colleague whom I follow on Twitter brought an important paper to my attention. I thought I’d share it more widely.</p>
<div>The <a rel="nofollow" href="https://www.tandfonline.com/doi/abs/10.1080/07350015.2016.1247004?journalCode=ubes20" target="_blank">paper</a> is titled, “<span>Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effect </span><span>models”, by </span><a rel="nofollow" href="https://www.jepusto.com/" target="_blank">James Pustejovski</a> (<a rel="nofollow" href="https://twitter.com/search?q=%40jepusto&src=typd" target="_blank">@jepusto</a>) and <a rel="nofollow" href="https://www.bethtipton.com/" target="_blank">Beth Tipton</a> (<a rel="nofollow" href="https://twitter.com/stats_tipton" target="_blank">@stats-tipton</a>). It appears in <a rel="nofollow" href="https://www.tandfonline.com/doi/abs/10.1080/07350015.2016.1247004?journalCode=ubes20" target="_blank"><i>The Journal of Business and Economic Statistics</i></a>.  </div>
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<div>You can tell right away, from its title, that this paper is going to be a must-read for empirical economists. And note the words, “Small-sample” in the title – that sounds interesting.</div>
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<div> Here’s a compilation of Beth’s six tweets:</div>
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<blockquote><p>“Econ friends, <a rel="nofollow" href="https://twitter.com/search?q=%40jepusto&src=typd" target="_blank">@jepusto</a> and I have a new paper out that we would love to share. It’s about clustering your standard errors (more below).</p></blockquote>
<blockquote><p><span>‏Any suggestions for how to get these methods out to economists given that we aren’t NBER?</span></p></blockquote>
<blockquote><p>Summary: Our paper provides small-sample adjustments to cluster robust variance estimation (CRVE). It can be used with panel data, experimental data, and regression. You can implement the method in a Stata macro called <a rel="nofollow" href="http://fmwww.bc.edu/RePEc/bocode/r/reg_sandwich.ado" target="_blank">REG_SANDWICH</a> and an R package called <a rel="nofollow" href="https://cran.r-project.org/web/packages/clubSandwich/clubSandwich.pdf" target="_blank">clubSandwich</a>.  </p></blockquote>
<blockquote><p>Why do you need this? Regular CRVE doesn’t do so well, even with as many as 100 clusters (!). In fact, CRVE only gives you appropriate Type I error when your covariates are balanced. </p></blockquote>
<blockquote><p>What did we do? We extended the bias-robust linearization method (BRL) by Bell &amp; McCaffrey in three ways: (1) in addition to a t-test, there is now an F-test; (2) We can handle the inclusion of fixed effects; (3) You get the same results whether you use FE or absorption. </p></blockquote>
<blockquote><p>How does it work? The adjustment inflates the standard errors a small bit. But more importantly, it provides Satterthwaite-type degrees of freedom that are more appropriate. The result is a test we call the ‘Approximate Hotelling’s T-squared’ (AHT) test. </p></blockquote>
<blockquote><p>We’d love to share the work broadly, so if you have ideas, please let us know. Thanks!”</p></blockquote>
<div>I’ve added the links to the R and Stata software in the quote above. </div>
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<div><span>There are also some nice R vignettes available:</span></div>
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<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/clubSandwich/vignettes/Robust-Variance-Estimation-with-clubSandwich.html" target="_blank">Cluster-robust variance estimation with clubSandwich</a></li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/clubSandwich/vignettes/meta-analysis-with-CRVE.html" target="_blank">Meta-analysis with cluster-robust variance estimation</a></li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/clubSandwich/vignettes/panel-data-CRVE.html" target="_blank">Cluster-robust standard errors and hypothesis tests in panel data models</a></li>
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<div>Well, Beth, who can resist a club sandwich? I don’t know if this post will help you and James, but I do hope so. </div>
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<div>These new results, and the associated software certainly deserve to be publicized, <i>and used</i>, widely.</div>
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<div>Check them out!</div>
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<div>(<i>Disclaimer</i>: <i>I do not know either James of Beth, personally or professionally</i>.)</div>
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<p><center>© 2018, David E. Giles</center></div>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://davegiles.blogspot.com/2018/11/more-sandwiches-anyone.html"> Econometrics Beat: Dave Giles' Blog</a></strong>.</div>
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