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Title: Nonparametric estimation of the variance function in an explosive autoregressive model Authors:  Yang Zu - University of Nottingham (United Kingdom) [presenting]
Dave Harvey - University of Nottingham (United Kingdom)
Steve Leybourne - University of Nottingham (United Kingdom)
Abstract: Nonparametric estimation is considered for the innovation variance function in an autoregressive model that can exhibit unit root, explosive and stationary regimes, allowing for behaviour often seen in financial data where bubble and crash episodes are present. The model permits multiple regime changes occurring at unknown points in time. Extant variance function estimators lack consistency for our model. We thus propose a new truncation-based kernel smoothing estimator, which we show is uniformly consistent for the innovation variance function. In a Monte Carlo simulation, we study the finite sample performance of our estimator and highlight the role of truncation in increasing the variance estimation accuracy.