Nonparametric Intervention Time Series Modeling

• Activity Number: 548
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Jin-Hong Park*+
• Companies: College of Charleston
• Keywords: nonparametric intervention analysis ; central mean subspace in time series ; event study ; dimension reduction ; Nadaraya-Watson kernel estimator

Abstract

Time series are often affected by interventions such as strikes, earthquakes, or policy changes. In this talk, I introduce a practical nonparametric intervention model using the central mean subspace in time series. I estimate the central mean subspace for time series taking into account known interventions by using the Nadaraya Watson kernel estimator. I use the modified Bayesian information criterion to estimate the unknown lag and dimension. Finally, I demonstrate that this nonparametric approach for intervened time series performs well in simulations and in a real data analysis such as the Monthly average of the oxidant.