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Markov switching time series models with application to a daily runoff series
Author(s) -
Lu ZhanQian,
Berliner L. Mark
Publication year - 1999
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/98wr02686
Subject(s) - series (stratigraphy) , autoregressive model , surface runoff , markov chain , time series , bayesian probability , gibbs sampling , markov process , computer science , econometrics , mathematics , environmental science , statistics , geology , paleontology , ecology , biology
We consider a class of Bayesian dynamic models that involve switching among various regimes. As an example we produce a model for a runoff time series exhibiting pulsatile behavior. This model is a mixture of three autoregressive models which accommodate “rising,” “falling,” and “normal” states in the runoff process. The mechanism for switching among regimes is given by a three‐state Markov chain whose transition probabilities are modeled on the basis both of past runoff values and of a time series of rainfall data. We adopt the Bayesian approach and use the Gibbs sampler in the numerical analyses. A study of a daily runoff series from Lake Taupo, New Zealand, is given.