Petrova, K., "Uniform Inference with General AR(p) Processes"
Title: "Uniform Inference with General AR(p) Processes"
(co-authored with Tassos Magdalinos)
Speaker: Associate Professor Katerina Petrova, Ca’ Foscari University of Venice
Host: Assistant Professor Alexopoulos Angelos, Department of Economics, Athens University of Economics and Business
Room: 76, Patission Str., Derigny Wing, 4th floor, Room D4.
Abstract: A unified theory of estimation and inference is developed for an AR(p) process with p distinct roots in ( -∞, ∞) which include roots in the stationary, local-to-unity, explosive and all intermediate regions. We propose a novel estimation procedure, based on an artificially constructed AR(p) process built with data-driven combination of a near-stationary and a mildly explosive roots and used as an instrumental variable. Our IV procedure delivers mixed-Gaussian limit theory and gives rise to an asymptotically standard normal t-statistic and x₂ Wald statistics across all autoregressive regions. The resulting hypothesis tests and confidence intervals are shown to have correct asymptotic size (uniformly over the space of autoregressive parameters and the space of innovation distribution functions) in autoregressive and predictive regression models, thereby establishing a general and unified framework for inference with autoregressive processes. Extensive Monte Carlo simulation shows that the proposed methodology exhibits very good nite sample properties over the entire Autoregressive parameter space ( -∞, ∞)
