In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares. We were recently alerted that Theorem 2 in the paper is incorrect as stated. It turns out, the conditions in the original version of the theorem are too general. A more limited version of the Theorem does actually hold, but only for models estimated using ordinary (unweighted) least squares, under a working model that assumes independent, homoskedastic errors. In this post, I'll give the revised theorem, following the notation and setup of the previous post (so better read that first, or what follows won't make much sense!).

In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares. A careful reader recently alerted us to a problem with Theorem 2 in the paper, which concerns a computational short cut for a certain cluster-robust variance estimator in models with cluster-specific fixed effects. The theorem is incorrect as stated, and we are currently working on issuing a correction for the published version of the paper. In the interim, this post details the problem with Theorem 2. I'll first review the CR2 variance estimator, then describe the assertion of the theorem, and then provide a numerical counter-example demonstrating that the assertion is not correct as stated.

I’m just back from the Society for Research on Educational Effectiveness meetings, where I presented work on small-sample corrections for cluster-robust variance estimators in two-stage least squares models, which I’ve implemented in the clubSandwich R package.

In settings with independent observations, sample size is one way to quickly characterize the precision of an estimate. But what if your estimate is based on weighted data, where each observation doesn’t necessarily contribute to equally to the estimate?

Regression discontinuity designs (RDDs) are now a widely used tool for program evaluation in economics and many other fields. RDDs occur in situations where some treatment/program of interest is assigned on the basis of a numerical score (called the running variable), all units scoring above a certain threshold receiving treatment and all units scoring at or below the threshold having treatment withheld (or vice versa, with treatment assigned to units scoring below the threshold).

NOTE (2019-09-24): This post pertains to version 0.56 of the rdd package. The problems described in this post have been corrected in version 0.57 of the package, which was posted to CRAN on 2016-03-14.

I’ve recently been working with my colleague Beth Tipton on methods for cluster-robust variance estimation in the context of some common econometric models, focusing in particular on fixed effects models for panel data—or what statisticians would call “longitudinal data” or “repeated measures.

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