similar to: Kalman Filter

Hi all R gurus out there, Im a kind of newbie to kalman-filters after some research I have found that the dlm package is the easiest to start with. So be patient if some of my questions are too basic. I would like to set up a beta estimation between an asset and a market index using a kalman-filter. Much littarture says it gives superior estimates compared to OLS estimates. So I would like to

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,

Hi, i'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, I'm looking for a example of transformation of ARIMA in a state space equivalent to use the dlm package and calcualte the hyperparameters. Someone can help me about it?. If it's possible with a arima(1,0,1) example, or more complex model. While I have more examples best for me.

Dear R community, I'm starting to learn the MS-VAR methodology and I would like to know what I need to download (e.g. packages) to make MS-VAR estimations using R. Best, Henrique C. de Andrade Doutorando em Economia Aplicada Universidade Federal do Rio Grande do Sul www.ufrgs.br/ppge [[alternative HTML version deleted]]

Dear all, I have a matrix as follows: a=matrix(c(1,2,3,1,2,3,1,2,3,6,5,7,7,5,7,5,6,5,"Y", "N","Y","Y","N","Y", "N","Y","Y"),ncol=3) > a [,1] [,2] [,3] [1,] "1" "6" "Y" [2,] "2" "5" "N" [3,] "3" "7"

I uploaded a new version of package dlm to CRAN. dlm provides functions for maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Gaussian linear state space models, also known as Dynamic Linear Models. The most important visible changes from the previous version are the following. 1) Missing values are now allowed in the observations. 2) Extractor and replacement

I uploaded a new version of package dlm to CRAN. dlm provides functions for maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Gaussian linear state space models, also known as Dynamic Linear Models. The most important visible changes from the previous version are the following. 1) Missing values are now allowed in the observations. 2) Extractor and replacement

Hello all, I am working with the dlm package, specifcially doing a dlm multivariate Y linear regression using dlmModReg and dlmFilter and dlmSmooth... I have altereted the inputs into dlmModReg to make them time-varying using JFF, JW etc. How do I track the results of the time varying system matrices? For example what I am really interested in is JW - my system variance matrix for each time

Hello, I'm apologize to have made failure before. I wrote this : p<-scan("teststat.txt") on R and R returns Error in scan ("teststat.txt") : "scan" expected a real, got "x". I don't really understand,because teststat has been created, so........ Thanks a lot. Sandrine

I am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

help.search("Kalman") says to look at help(KalmanLike, package=ts). Andy -----Original Message----- From: Mohamed A. Kerasha [mailto:mohamed at engr.uconn.edu] Sent: Tuesday, November 19, 2002 9:27 AM To: r-help at stat.math.ethz.ch Subject: [R] Kalman Filter Hi all, Does any one know if there is Kalman Filter code or library in R. Thanks, Mohamed.

Dear R users, Just to clarify. I am not offering to pay someone to do my Dissertation. These 4-5 commands on Kalman Filter would be only a tiny part of my 10,000 words dissertation. A part that even after trying for a few days, I am still stuck on. I am offering ?50, just to say thanks. Regards -- View this message in context:

Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? -

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

I have a problem involving state space models with a multivariate observation equation. In other words: the kalman filtering routines as implemented in the package ts cannot be used since it treats the univariate case only. My question : does a multivariate kalman filtering procedure for R exist somewhere in the world? Where could I perhaps expect to find something like that? Many thanks M.

If you mean this paper by Fahrmeir: http://biomet.oxfordjournals.org/cgi/content/abstract/81/2/317 I would recommend BayesX: http://www.stat.uni-muenchen.de/~bayesx/. BayesX interfaces with R and estimates discrete (and continuous) time survival data with penalized regression methods. If you are looking for a bona fide Bayesian survival analysis method and do not wish to spend a lot of time

Hi My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am trying to implement Kalman Filter into my school work. I have some problems with understanding of R version of Kalman Filter in package stats( functions KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). 1) Can you tell me how are you seting the initial values of state vector in Kalman Filter? Are you using some method?

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)