Drivers Licenses for Unauthorized Immigrants and Auto Insurance Coverage (2020) (with Brandyn Churchill and Taylor Mackay) , Contemporary Economic Policy 39(1): 107-125. Previous version published as CGO Working Paper 2020.009.
Empirical likelihood and uniform convergence rates for dyadic kernel density estimation (with Harold D. Chiang)
This paper studies the asymptotic properties of and improved inference methods for kernel density estimation (KDE) for dyadic data. We first establish novel uniform convergence rates for dyadic KDE under general assumptions. As the existing analytic variance estimator is known to behave unreliably in finite samples, we propose a modified jackknife empirical likelihood procedure for inference. The proposed test statistic is self-normalised and no variance estimator is required. In addition, it is asymptotically pivotal regardless of presence of dyadic clustering. The results are extended to cover the practically relevant case of incomplete dyadic network data. Simulations show that this jackknife empirical likelihood-based inference procedure delivers precise coverage probabilities even under modest sample sizes and with incomplete dyadic data. Finally, we illustrate the method by studying airport congestion.
Work in Progress
Estimating heterogeneous treatment effects in unobserved networks: An application of the elastic net to regional policy evaluation (with Matthew Zaragoza-Watkins)