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Title: Multistep Yule-walker estimation of autoregressive models
Keywords: Time series, Autoregressive Model, Least Square method, Yule-Walker Method, ACF
Issue Date: 26-Jul-2010
Citation: YOU TINGYAN (2010-07-26). Multistep Yule-walker estimation of autoregressive models. ScholarBank@NUS Repository.
Abstract: The aim of this work is to fit a ?wrong? model to an observed time series by employing higher order Yule-Walker equations in order to enhance the fitting accuracy. Several parameter estimation methods for autoregressive models were reviewed, such as Maximum Likelihood method, Least Square method, Yule-Walker method, Burg?method, etc. Comparison of the estimation accuracy between the well-known Yule-Walker method and our new multistep Yule-Walker method based on the autocorrelation function (ACF) is made. The effect of different number of Yule-Walker equations on the estimation performance is investigated. Monte Carlo analysis and real data are used to check the performance of the proposed method.
Appears in Collections:Master's Theses (Open)

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