similar to: State Space models in R

Hello My name is greigiano am student of Applied Economics, Department of Rural Economy. I am working on an article forecasting, which use the dynamic linear model, a model state space. I am wondering all the commands in R, to represent the linear dynamic model and Kalman filter. I am available for any questions. -- DEUS Seja Louvado Que ELE Ilumine sua vida Assim como ELE tem Iluminado a Minha

I familarized myelf with kalmanlike and structts which are approaches for building and estimating ( and forecasting ) state space models ( or the equivalent arima models ). back in 2003, gavin simpson wrote an email describing the west and harrison apprach to estimate state space models and asked if anything was out there for using that approach. the goals of this approach are the same as kalman

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

Dear All, I am trying to fit a structural time series model using the StructTS function (package stats) with only 2 seasons (summer and winter). More than 2 seasons work fine but with 2 seasons I get this error: > fit <- StructTS(y.ts, type="BSM") Error in T[cbind(ind + 1L, ind)] <- 1 : subscript out of bounds I have looked at Prof. Ripley's 2002 RNews article but cannot

Hi, I'm trying to use the kalman filter to estimate the variable drift of a random walk, given that I have a vector of time series data. Anyone have any thoughts on how to do this in R? Thanks, Alex [[alternative HTML version deleted]]

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?

Hi, Following convention below: y(t) = Ax(t)+Bu(t)+eps(t) # observation eq x(t) = Cx(t-1)+Du(t)+eta(t) # state eq I modified the following routine (which I copied from: http://www.stat.pitt.edu/stoffer/tsa2/Rcode/Kall.R) to accommodate u(t), an exogenous input to the system. for (i in 2:N){ xp[[i]]=C%*%xf[[i-1]] Pp[[i]]=C%*%Pf[[i-1]]%*%t(C)+Q siginv=A[[i]]%*%Pp[[i]]%*%t(A[[i]])+R

Hi All, I have a problem trying to forecast using the dlm package, can anyone offer any advise? I setup my problem as follows, (following the manual as much as possible) data for example to run code CostUSD <- c(27.24031,32.97051, 38.72474, 22.78394, 28.58938, 49.85973, 42.93949, 35.92468) library(dlm) buildFun <- function(x) { dlmModPoly(1, dV = exp(x[1]), dW = exp(x[2])) } fit <-

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? -

Hi - I read through dse package manual a bit. I'm not quite certain how I can use it to estimate a time varying coefficient regression model? I might pick up an inappropriate package. Any suggestion would be greatly appreciated. Thank you. rh

Hello, I am writing a package called 'DLM' containing a vignette. The vignette contains a chunck with the function call 'library(DLM)'. This worked fine with 'R CMD check DLM', but when it comes to building the package with 'R CMD build DLM' I get the following error message: * creating vignettes ... ERROR Error: chunk 1 Error in library(DLM) : There is no

Hello, How can I see a function called "+.dlm"? > methods("+") [1] +.Date +.dlm* +.POSIXt Non-visible functions are asterisked > getAnywhere("+.dlm") Error in grep(pattern, x, ignore.case, extended, value, fixed, useBytes) : invalid regular expression '+\.dlm' Thanks in advance, Giovanni -- Giovanni Petris <GPetris at uark.edu>

Hi, I am studying Kalman Filter and it seems to be difficult for me to apply the filter on a simple ARMA. It is easy to construct the state-space model, for instance: dlmModARMA(ar=c(0.4,-0.2),ma=c(0.2,-0.1, sigma2=1) but applying the dlmFilter on it, it doesn't work... I don't know if my problem is clear but if anyone has already worked on Kalman filter, it could be great to advise me!

I have a bunch of clean timeseries data obtained from a sensor and I'd like to apply a Kalman Filter to it to smoothe it out. Through a few days of Googling, reading papers, implementing such a filter in various languages, I finally realised that it may be built into R. So I did a "??kalman" at the R prompt and found that it is indeed there. However, the help page is a tad bare,

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

Hello, Does anybody know if (and how) it is possible to obtain standard errors of estimated variances from StructTS? (R 2.10.0). Thank you in advance, Giovanni

Hi, I am trying to figure how to use dlm with constant terms (possibly time-dependent) added to both equations y_t = c_t + F_t\theta_t + v_t \theta_t = d_t + G_t\theta_{t-1} + w_t, in the way that S-PLUS Finmetrics does? Is there any straightforward way to transform the above to the default setup? Thanks, Tsvetan -------------------------------------------------------- NOTICE: If received in

Is there a way to add covariates to the system equation in a time-varying approach: Y[t] = F'[t]theta[t] + v[t], v[t] ~ N[0,V] #observation equation theta[t] = theta[t-1] + psi*Z[t] + w[t], w[t] ~ N[0,W] #system equation While F[t] is a matrix of regressors to capture the short term effect on the response series Y, Z[t] measures the long-term effect of either (1) two policies by a step

>From: "Ivaha C (AT)" <civaha at glam.ac.uk> >Date: 2008/04/10 Thu AM 08:51:14 CDT >To: R Help <r-help at r-project.org> >Subject: [R] Structural Modelling in R-project if you have a univariate time series and you want to break it into its various components, then you get the scalars based on a decomposition. if you have a kalman filter/ basic strucutural

I'm searching a method to estimate the hyper-parameters in arima models. I'm reading about r-inla package, but in the examples section only talk about the AR part of the arima, but i need help about the MA part too. I'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, but the computacional cost of inla i think is better, but only