Structural Change and Time Series Analysis
This investigation introduces changing-parameter ARMA processes as a way to model a time series. Many time series exhibit a changing trend or a changing autocorrelation structure; that is to say, they have certain nonstationary characteristics that cannot be modeled by the usual ARMA representation. The analysis of a changing parameter process is accomplished by a Bayesian approach, where the posterior distributions of the parameters are derived, and the analysis is illustrated with a moving average model that has a changing autocorrelation function.
KeywordsPosterior Distribution Bayesian Analysis Time Series Analysis Shift Point Auto Covariance Function
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