Inference with Dependent Data Using Cluster Covariance Estimators

Joint with: C. Alan Bester and Timothy G. Conle

This paper presents a novel way to conduct inference using dependent data in time series, spatial, and panel data applications. Our method involves constructing t and Wald statistics utilizing a cluster covariance matrix estimator (CCE). We then use an approximation that takes the

number of clusters/groups as fixed and the number of observations per group to be large and calculate

limiting distributions of the t and Wald statistics. This approximation is analogous to `fixed-b'

asymptotics of Kiefer and Vogelsang (2002, 2005) (KV) for heteroskedasticity and autocorrelation consistent inference, but in our case yields standard t and F distributions where the number of groups essentially plays the role of sample size. We provide simulation evidence that demonstrates

our procedure outperforms conventional inference procedures and performs comparably to KV.

For more information, contact Frank Schorfheide.