Seminar by Dr. Keli XU, Department of Economics, Indiana University

This paper provides the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown, possibly infinity. The theory allows for various persistence levels of the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two contributions. First, we show that LPs can be semiparametrically efficient under classical assumptions on data and horizons if the controlled lag order diverges. Thus the commonly perceived efficiency loss of running LPs can be asymptotically negligible with many controls. Second, we propose LP-based inferences for (level and cumulated) impulse responses with robustness properties not shared by other existing methods. Inference methods using two different standard errors are considered, and neither involves HAR-type correction. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while the validity for the second holds more generally for martingale-difference heteroskedastic shocks.

Keli obtained Ph.D. at Yale University in 2007 and currently is a professor at the Department of Economics, Indiana University. Before that, he was Associate Professor of Economics and Rothrock Fellow of Liberal Arts at Texas A&M University, and Assistant Professor of Finance and Management Science and Pearson Fellow and Canadian Utilities Fellow of School of Business at University of Alberta, Canada. Dr. Xu is a Fellow of the Journal of Econometrics, a recipient of the Multa Scripsit award from Econometric Theory, and an Associate Editor of Econometric Reviews. His research focuses on econometrics, developing statistical methodologies, and theories to analyze economic models and data.