similar to: Package dlm generates unstable results?

  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,

Hello, I would like to use modFit and modCost from the package FME to find the optimal initial time t0 of a process. For simplicity, the process is either "off" (value 0) or "on" (value h). So I have a data vector with some zeros followed by some h's, e.g. > c(0,0,0,2,2,2,2,2,2,2) [1] 0 0 0 2 2 2 2 2 2 2 (hence h=2 here). I want to find the best guess for the initial

* Apologies for multiple posting * I attached to my previous e-mail a .r file, and it was not permitted by the rules of the mailing lis. Again, please receive my sincere apologies for this. I re-send again the e-mail with .txt attachemnt in the hope someone an help me to solve my problem. I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I

Dear all, I used modFit of the package FME to fit a set of ODE to a ste of eperiemntal data. The summary of this fit give me the following error > summary(Fit) Residual standard error: 984.1 on 452 degrees of freedom Error in cov2cor(x$cov.unscaled) : 'V' is not a square numeric matrix In addition: Warning message: In summary.modFit(Fit) : Cannot estimate covariance; system is

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

Hi, I was trying to repeat the estimation of threshold GARCH models from the book "Analysis of Financial Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2

Dear R users, I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I wrote using modFit and modCost of FME package and the file of the time series. When I run summary(Fit) I obtain this error message, and the values of the parameters are equal to the initial guesses I gave to them. The problem is not due to the fact that I have only one

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

  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, all I can not figure this out, please have a look and help me out. thank you! Note: this is in SPLUS, not R. I have following code *********************************** modfit<-function(yir,yew, ft) { n<-length(yew) yew<-yew[1:(n-1)] yy<-yir-ft xx<-yew-ft n<-length(xx) xx0<-xx[2:n] yy0 <-yy [2:n] xx1<-xx[1:(n-1)] fit <- garch(yy0~xx0 + xx1+var.in.mean,

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

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, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Hi, I've been searching CRAN and the web for a recursive multivariate filter with time-varying coefficients. What I mean is the following: I have a series of square matrices A_t an initial value vector y_0 and I need to compute y_t =A_t%*%y_t-1 As these y_t may diverge quickly and/or lead to underflow problems, the y_t need to be scaled by eg y_t =y_t/sum(y_t-1) Is anyone aware

Dear R People: Suppose I want to estimate the parameters of the following AR model: (1 - phi_1 B - phi_2 B^2 - phi_9 B^9) x_t = a_t and I want to use the arima0 command from the ts library. How would I use the order subcommand, please? R Version 1.4.1 for Windows. Thanks! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

This is not so much a bug as an infelicity in the code that can easily be fixed. The initialize expression in the quasi family function is, (uniformly for all links and all variance functions): initialize <- expression({ n <- rep.int(1, nobs) mustart <- y + 0.1 * (y == 0) }) This is inappropriate (and often fails) for variance function "mu(1-mu)".

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

Hi..   R Program is shown ARIMA output as below then SARIMA equation is be   (1 - 0.991B^{12})z_t + 43.557 = (1+0.37B)(1-0,915B^{12})a_t    But I try to calculate it by manual . It look like it 's big different from R sofeware,   I am not sure this equation is correct or not . PLS supoort me to confirm it   Arima Model ( 0,0,1)(1,0,1)   No Transformation Constant   >> 43.557 , t = 10.09

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.

I'd like to estimate time varying coefficients in a linear regression using a Kalman filter. Even if the Kalman Filter seems to be available in some packages I can't figure out how to use it to estimate the coefficients. Is there anyway to do that in R? Any help appreciated Thanks