similar to: Anyone Familiar with Using arima function with exogenous variables?

>From the help document on KalmanLike, KalmanRun, etc., I see the linear Gaussian state space model is a <- T a + R e y = Z' a + eta following the book of Durbin and Koopman. In practice, it is useful to run Kalman filtering/smoothing/forecasting with exogenous factor: a <- T a + L b + R e y = Z' a + M b + eta where b is some known vector (a function of time). Some other

Is there any package for Vector Auto-regressive with exogenous variable other than fastVAR? Because it is not able to solve my problem of not taking the base in the model. Please suggest some appropriate solution!!!! -- View this message in context: http://r.789695.n4.nabble.com/Is-there-any-package-for-Vector-Auto-regressive-with-exogenous-variable-other-than-fastVAR-tp4644964.html Sent from

Hello, I have the similar issue in estimating a GARCH model with exogenous variables in the mean equation. Currently, to my understanding, the garch function in tseries package can handle univariate model, and garchFit in fGarch can handle ARMA specification. I wonder if there is any R function that can handle exogenous variables in estimating GARCH? Thank you a lot. Edwin -- View this

Hi, I am trying to fit all subsets for a vector autoregression with exogenous variables. I have been looking at the 'leaps' function but I not sure how to get it to work when lags for each variable are included in the model. I would be really appreciative if someone could provide some links to examples. Thanks in advance! -- View this message in context:

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

Anyone know how to esimate parameters in the system: x[k]=Ax[k-1]+ B + Gv[k-1] y[k]=x[k]+Du[k]+Hw[k] a system with exogenous u[k] in the measurement eq., v,w are iid, both eq. are gaussian. Thanks, Oyvind --------------------------------- [[alternative HTML version deleted]]

Hello all I'm trying to run some path analysis in either sem or lavaan (preferably lavaan because I find its interface easier to use). Most of my variables are continuously distributed and fairly well-behaved but I have a single exogenous variable (sex) which is not continuously distributed. Preliminary model fitting suggests that there aren't any sex by (anything else) interactions. The

Dear R-list, I would like to show the implications of estimating a linear trend to time series, which contain significant serial correlation. I want to demonstrate this, comparing lm() and an gls() fits, using the LakeHuron data set, available in R. Now in my particular case I would like to draw confidence bands on the plot and show that there are differences. Unfortunately, I do not know how to

Good afternoon! I'm trying to learn time series but i have a bit of of a problem using R packages for this. 1. > LakeHuron > sample(500:600, 98) > sample(500:600, 98)->t > fit<-arima(LakeHuron, order=c(2,1,1), xreg=t) > fit > predict(fit, n.ahead=1, newxreg=t) Now, my problem is this: is it ok to use the same t in predict function or should my newxreg contain 99

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

hi, i am trying to teach myself about ARIMA models. i have followed examples from a number of sources and have more or less got the hang of how it works. i would like to compare the output from the fitted model to the original data. is this possible? or even a meaningful thing to do? to be clear, for example, having generated a fit to some data using > fit <- arima(LakeHuron, order = c(1,

According to Hadley's ggplot book (p. 109), both the graphs below should have a legend, and yet none appears in my hands. Any suggestions? I can't see a typo. Is there a bug? library(ggplot2) data(LakeHuron) huron = data.frame(year=1875:1972,level=LakeHuron) p = ggplot(huron, aes(year)) + geom_line(aes(y= level - 5), colour = 'blue') + geom_line(aes(y= level + 5), colour

Hi List l am interested in developing price model. I have found a research paper related to price model of corn in US market where it has taken demand & supply forces into consideration. Following are the equation: Supply equation: St= a0+a1Pt-1+a2Rt-1+a3St-1+a5D1+a6D2+a7D3+U1 -(1) Where D1,D2,D3=Quarterly Dummy Variables(Since quarterly data are considered) Here, Supply

I am fitting a reduced form VAR model using VAR in the vars library. I have several endogenous variables, and two exogenous variables. I would like to explore the effects of a shock to one of the exogenous variables on one of the endogenous variables. Using irf in the vars library only calculates the irf for the endogenous variables, this is obviously by design, is there some theoretical

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

Hi, I have the following problem. It has two parts. 1. I need to calculate the stationary probabilities of a Markov chain, eg if the transition matrix is P, I need x such that xP = x in other words, the left eigenvectors of P which have an eigenvalue of one. Currently I am using eigen(t(P)) and then pick out the vectors I need. However, this seems to be an overkill (I only need a single

Hello All, I am trying to create a legend for a black-white graph. The package I use is ggplot2. It can add colors to the legend key but not line types. Can you please help? # example from Wickman (2009, ggplot2 - elegant graphics for data analysis, page 109) library(ggplot2) huron <- data.frame(year=1875:1972, level=LakeHuron) ggplot(huron, aes(year)) +

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

I'm using the arima function to estimate coefficients and also using predict.Arima to forecast. This works nicely and I can see that the results are the same as using SAS's proc arima. I can also take the coefficent estimates for a simple model like ARIMA(2,1,0) and manually compute the forecast. The results agree to 5 or 6 decimal places. I can do this for models with and without

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),