similar to: Help on State-space modeling

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

Dear all, I am currently trying to use the "dlm" package for Kalman filtering. My model is very simple: Y_t = F'_t Theta_t + v_t Theta_t = G_t Theta_t-1 + w_t v_t ~ N(0,V_t) = N(0,V) w_t ~ N(0,W_t) = N(0,W) Y_ t is a univariate time series (1x1) F_t is a vector of factor returns (Kx1) Theta_t is the state vector (Kx1) G_t is the identity matrix My first

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

Dear r-users! I have another question regarding the dlm package and I would be very happy if someone could give me a hint! I am using the dlm package to get estimates for an endogenous rate of capacity utilization over time. The general form of a state space model is (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) The

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)

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly

Dear R users, suppose we have a random walk such as: v_t+1 = v_t + e_t+1 where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and v_t is the fundamental value of a stock. Now suppose I want a trading strategy to be: x_t+1 = c(v_t – p_t) where c is a costant. I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only

hi: you should probably send below to R-Sig-Finance because there are some econometrics people over there who could also possibly give you a good answer and may not see this email ? Also, there's package called mar ( I think that's the name ) that may do what you want ? Finally, I don't know how to do it but I think there are ways of converting a multivariate arima into the

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression

Hi, I would like to know how (if) I can extract some of the information from the summary of my nlme. at present, I get a summary looking something like this: > summary(fit.nlme) Nonlinear mixed-effects model fit by maximum likelihood Model: MLKYLD ~ W4(DIM, logA, B, C) Data: ADHIS.x0 AIC BIC logLik 265314 265401.6 -132647 Random effects: Formula: list(logA ~ 1 , B ~

Dear R help, This seems to be a commonly asked question and I am able to run examples that have been proposed, but I can't seems to get this to work with my own data. Reproducible code is below. Thank you in advance for any help you can provide. The main problem is that I can not get the confidence lines to plot correctly. The secondary problem is that predict is not able to find my object

Hi All, I'm trying to do a Scatterplot (package: car), and add a line (just for reference). There is my code: #------------------------------------Code--------------------------------------------------- library("car") library("calibrate") G_T<-c("car","bike","boat") ave<-c(80,10,45) perf<-c(100,80,75) df2<-data.frame(G_T,ave,perf)

Hi I'm sorry about the triviality of my problem. I have a vector (v) of three columns (logA, logB, id). I want to compute (and plot) the correlation between logA and logB for different thresholds of id (e.g. >30, etc). So I tried: for(i in 1:100){ points(cor(v$logA[v$id>i], v$logB[v$id>i], use="complete.obs"), i)) } (i created a plot object already) but it comes with

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

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

Hello, R community, I have been using the lmer and mcmcsamp functions in R with some difficulty. I do not believe this is my code or data, however, because my attempts to use the sample code and 'sleepstudy' data provided with the lme4 packaged (and used on several R-Wiki pages) do not return the same results as those indicated in the help pages. For instance: > sessionInfo() R

Is there an R-language interface to the R API C-language functions Rf_logspace_add() and Rf_logspace_sub()? I don't see one but I may not looking under the right name. Various packages have functions which do that same sort of thing (log(exp(x)+exp(y)) and log(exp(x)-exp(y)) without unnecessary floating point errors). They have names like matrixStats::logSumExp(lx, na.rm=FALSE, ...)