Skip to main content


eCommons@Cornell >
College of Engineering >
Operations Research and Information Engineering >
ORIE Technical Reports >

Please use this identifier to cite or link to this item:
Title: Establishing Stationarity of Time Series Models via Drift Criteria
Authors: Woodard, Dawn
Matteson, David
Henderson, Shane
Keywords: time series models
drift criteria
Lyapunov function techniques
Issue Date: 6-May-2010
Abstract: Time series models are often constructed by combining nonstationary effects such as trends with stochastic processes that are known (or believed) to be stationary. However, there are numerous time series models for which the stationarity of the underlying process is conjectured but not yet proven. We give an approachable introduction to the use of drift criteria (also known as Lyapunov function techniques) for establishing strict stationarity and ergodicity of such models. These conditions immediately imply consistent estimation of the mean and lagged covariances, and more generally the expectation of any integrable function. We demonstrate by proving stationarity and ergodicity for several novel and useful examples, including Poisson log-link Generalized Autoregressive Moving Average models.
Description: ORIE Technical Report 1477
Appears in Collections:ORIE Technical Reports

Files in This Item:

File Description SizeFormat
DriftCriteria.pdf131.37 kBAdobe PDFView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.


© 2014 Cornell University Library Contact Us