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? Here, one useful way to gauge the precision of an estimate is the effective sample size or ESS. Suppose that we have (N) independent observations (Y_1,…,Y_N) drawn from a population with standard deviation (\sigma), and that observation (i) receives weight (w_i).

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).

The rdd package in R provides a set of methods for analysis of regression discontinuity designs (RDDs), including methods to estimate marginal average treatment effects by local linear regression. I was working with the package recently and obtained some rather counter-intuitive treatment effect estimates in a sharp RDD model. After digging around a bit, I found that my perplexing results were the result of a subtle issue of model specification. Namely, in models with additional covariates (beyond just the running variable, treatment indicator, and interaction), the main estimation function in rdd uses a specification in which covariates are always interacted with the treatment indicator.

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.” We have a new working paper, which you can find here.
The importance of using CRVE (i.e., “clustered standard errors”) in panel models is now widely recognized. Less widely recognized, perhaps, is the fact that standard methods for constructing hypothesis tests and confidence intervals based on CRVE can perform quite poorly in when you have only a limited number of independent clusters.

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