similar to: dlm package: how to specify state space model?

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

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 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)

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

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

Hi all, I'm working on a term structure estimation using state-space modeling for 1, 2 and 3 factor models. When I started to read the functions on R, I got to the function ss on the library sspir. From what I understood this function is similar to SsfFit from S-PLUS. But for my models purpose there is something left to be desired. Its formulation follow these equations: *Y_t = F_t^T *

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

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 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

I am trying to use optim() to minimize a sum-of-squared deviations function based upon four parameters. The basic function is defined as ... SPsse <- function(par,B,CPE,SSE.only=TRUE) { n <- length(B) # get number of years of data B0 <- par["B0"] # isolate B0 parameter K <- par["K"]

I am new in R. I can simulate Arma, using Arima.sim However, I want to simulate an Arima Model. Say (1-B)Zt=5+(1-B)at. I do not know how to deal with 5 in this model. Can any one could help me? Thank you very much! Regards, -- View this message in context: http://www.nabble.com/simulate-arima-model-tp23239027p23239027.html Sent from the R help mailing list archive at Nabble.com.

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

I would like to know if there is a way to test no correlaction in time series ? cov(r_t, r_t-1)=0 And r_t are homoscedastik and independent. Thanks [[alternative HTML version deleted]]

Hi, After updating to R-3.0 beta r62328, I get the following: > install.packages("Biobase", type="source", repos="http://george2/BBS/2.12/bioc") Error in do.call(op, list(v_c, v_t[[op]])) : could not find function "R (>=2.15.1)" The problem can be fixed by adding a space after >= in the offending package's DESCRIPTION file and re-generating

Hello everybody, I've a question about "autoregressive Regressionmodels". Let Y[1],.....,Y[n], be a time series. Given the model: Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t, where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1) I want to estimate the coefficients phi and beta. Are in R any functions or packages for

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 want to fit a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

Hi all, My problem is as follows: I want to run a loop which calculates two values and stores them in vectors r and rv, respectively. They're calculated from some vector x with length a multiple of 7. x <- c(1:2058) I need to difference the values but it would be incorrect to difference it all in x, it has to be broken up first. I've tried the following: r <- c(1:294)*0 rv

Dear R-Helpers, I am looking for ways to assess quality of a predictor of variance of a random variable. Here a two related, but yet distinct, setups. 1. I observe y_t, t=1,...,T which is normally distributed with unknown variance v_t (note that the variance is time-dependent). I have two "predictors" for v_t, dubbed v1_t and v2_t, and I want to tell which predictor is better. Here

Dear all, I have a serious problem to solve my model. I study over exploitation of fish in the bay of biscay (france). I know only the level of catch and the fishing effort (see data below) by year. My model is composed by the following equations: * the growth function Gt(St) = r*St*(1-St/sbar) with Gt the growth of each period t r intrinsec growth of the stock sbar carriyng capacity of the