similar to: using dlmModPoly in library dlm

Dear R-help! I have encountered strange behaviour (that is, far-off filtering, smoothing and forecast distributions under certain conditions) in the `dlm` package by Giovanni Petris. Here is an example: I use the annual hotel bookings time series data, which I model using a second order polinomial DLM. First I perform the analysis with the data in logarithmic form and everything seems to be

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 <-

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 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

Dear R users, I am trying to use the dlm package, and in particular the dlmBSample function. For some reason that I am not able to understand, this function does not work properly and the plot of the result does not make sense, while dlmFilter works perfectly. I think that my_mod is correct, because the output of my_dlmFilter$mod is fine. Where is my mistake or my misunderstanding? This is the

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

Dear all: Greetings! I am trying to replicate a simple state space model in R, using the package 'dlm'. This model has two observation equations and three state equations. Each observation equation represents structural shocks based on SVAR for country i, where i=1 to 2. The structural shocks (S1 and S2) are to be decomposed into common (sv1) and country-specific (sv2, sv3) shocks. We

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 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 be very grateful if somebody more knowledgeable then me could assist me in the following. I have two (three actually but for simplicity I will say two) models which I would like to fit jointly as a state space object. Here are the equations: (1) w = a1 + b1*(p) + e1 a1 = a1[t-1] + g1 g1 = g1[t-1] + e2 b1 = b1[t-1] + e3 (2) d = a2 + b2*(w) + e3 a2 = a2[t-1] + e4 b2 = b2[t-1] + e5

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, Using Beamer, in order to highlight a piece of R code I do something like this - note the "\structure" and "\alert" commands: \begin{semiverbatim} > mleOut <- \structure{dlmMLE}(Nile, + parm = c(0.2, 120), # initial values for optimizer + lower = c(1e-7, 0)) \alert<2>{# V must be positive} > mleOut$convergence

This question pertains to setting up a model in the package "dlm" (dynamic linear models, http://cran.r-project.org/web/packages/dlm/index.html I have read both the vignette and?"An R Package for Dynamic Linear Models" (http://www.jstatsoft.org/v36/i12/paper), both of which are very helpful. There is also some discussion at

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

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

Folks: I've replaced an outer for-loop with lapply and it works great. But, I can't seem to do the following type of exception handling: tryCatch(dlmMLE(x)$value==Inf,error = function(e) NULL) which basically says if the likelihood is Inf, throw an error. But what I want it to do is just go to the next index in the list. When I was using a for-loop I used:

1. The function "KalmanLike" seems to change its inputs AND PREVIOUSLY MADE copies of the inputs. Consider the following (using R 2.1.0 patched under Windows XP): > Fig2.1 <- StructTS(x=Nile, type="level") > unlist(Fig2.1$model0[2:3]) a P 1120 286379470 > tst2 <- tst <- Fig2.1$model0 > tst23 <- tst[2:3] > tst23u <-