A0605
Title: Inference in time series models using smoothed clustered standard errors
Authors: Seunghwa Rho - Louisiana State University (United States) [presenting]
Timothy Vogelsang - Michigan State University (United States)
Abstract: A long run variance estimator is proposed for conducting inference in time series regression models that combines the traditional nonparametric kernel approach with a cluster approach. The basic idea is to divide the time periods into non-overlapping clusters. The long run variance estimator is constructed by first aggregating within clusters and then kernel smoothing across clusters. We develop an asymptotic theory for test statistics based on this smoothed clustered long run variance estimator. We derive asymptotic results holding the number of clusters fixed and also treating the clusters as increasing with the sample size. We find that the fixed number of clusters asymptotic approximation works well whether the number of clusters is small or large. Finite sample simulations suggest that clustering before kernel smoothing can reduce over-rejections caused by strong serial correlation without a great cost in terms of power. The simulations also suggest that the naive iid bootstrap mimics the fixed number of clusters critical values.
