| To: | Eric Zivot <ezivot@u.washington.edu>, s-news@lists.biostat.wustl.edu |
|---|---|
| Subject: | Re: Splus code for Dynamic Linear models |
| From: | yiwu ye <yiwu21111958@yahoo.com> |
| Date: | Thu, 16 Mar 2006 16:47:21 -0800 (PST) |
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| In-reply-to: | <002d01c64958$be8dc6b0$b102a8c0@zivotd800> |
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By Dynamic Linear Modeling, I mean a Bayesian Learning approach in linear forecasting, i.e. the DLM has a sequential nature in which the analysis proceeds. Starting with prior beliefs about the response series at time t-1, make a forecast for teh response at time t, and based on both the prior beliefs and the new observation, modify our beliefs regarding the response into posterior beliefs. The beliefs here can be expressed as a linear model. Hope this explains what I want a bit more. Thanks, Yiwu Eric Zivot <ezivot@u.washington.edu> wrote:
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