我也在寻找这样的大侠

要用卡尔曼滤波做一个东西，可是不太懂啊

那位高手请帮忙，先谢了

john black wrote: > > I've been using Proc Autoreg in SAS to regress a dependent variable on > time. Proc Autoreg takes care of the serial correlation in the error > term (I'm specifying an AR1 model). PA computes predicted values based > on the structural model and the full model. The structural piece uses > only the parameter estimates of the independent variables (no error > terms). The full model adds back the AR1 error term to the model. > > I'm having trouble reconciling the computation of the 1st predicted > value for the full model. SAS uses a Kalman filter to do this, and the > calculation appears complex. Can anyone explain how the calculation is > done for a simple (y regressed on time w/AR1 error term) regression, and > refer me to a good reference source. I have Hamilton's "Time Series > Analysis," but the discussion of the topic is convoluted. I'm looking > for a more basic intro to the topic. > > Thank you in advance, > > JBB

There are a lot of references for Kalman filters. The best source certainly is Peter S. Maybeck: ..Estimation and Control (first volume). Others would be Gelb: Applied optimal estimation, Jazwinski: Optimal Filtering,...

Christoph

[此贴子已经被作者于2005-4-4 2:27:01编辑过]

Example 10.2: Kalman Filtering: Likelihood Function Evaluation

Example 10.3: Kalman Filtering: Estimating an SSM Using the EM Algorithm

Example 10.4: Diffuse Kalman Filtering

[此贴子已经被作者于2005-4-4 2:23:50编辑过]

GaussX

Gaussx incorporates a full featured set of professional, state of the art, econometric routines that run under GAUSS. These tools can be used within Gaussx, both in research and in teaching. Alternatively, since the GAUSS source is included, individual econometric routines can be extracted and integrated in stand-alone GAUSS programs.

Kalman Filter

Analysis with the Kalman Filter allows for the estimation of state vectors, with smoothing, time varying transition matrices (ie. each element is a function), and the estimation of the elements of the Kalman matrices using ML. Stochastic Volatility models (SV) are estimated using quasi ML based on a Kalman Filter model.

Kalman Filtering: Theory and Practice Using MATLAB, 2nd Edition
Mohinder S. Grewal, Angus P. Andrews
ISBN: 0-471-39254-5
Hardcover
416 pages
January 2001