similar to: vector autoregression

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:

Folks, I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset): bivariateTS<-structure(c(0.950415958293559, 0.96077848972081, 0.964348957109053, 0.967852884998915, 0.967773510751625, 0.970342843688257, 0.97613937178359, 0.980118627997436, 0.987059493773907, 0.99536830931504, 1.00622672085718, 1.01198013845981, 1.01866618122606,

Hi! I'm a new R user and I have a question about estimating VAR on a panel data. What I'm trying to do is to explain stock's volume on it's lagged volume, it's lagged returns and lagged market return's (and vice versa). In addition I have generated an exogenous variable controlling for stock's volatility. Some of you may be familiar with this experiment since it follows

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 have obtained from transfer functions, the state space matrices for the following state space model. x* = Ax + Bu y = Cx + Du I have A, B, C, and D, now I would like to take the exogenous inputs and simulate the data using the state space model. I know there is a simulate function in the package dse1, but I am unsure as to what type of TSmodel to create to put into it. Could anyone give me

Dear users, I am struggling with this issue. I want to estimate a VAR(1) for three variables, say beta1 beta2 beta3, using monthly observations from January 1984 to September 2009. In-sample period January 1984 to December 2003, out-of-sample January 2004 to September 2009. This is what I have done at the moment

Hi R-Community, so far I dealt with univariate processes and used the function "arima" to estimate an ARMA(1,1)-model. For multivariate processes there are the functions "estVARXar" and "estVARXls" from package "DSE". But how can I estimate an VARMA(1,1)-model, or even better determine the orders and estimate the parameters? Much thanks in advance, Hagen

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

Hello, Using the dse package I have estimated a VAR model using estVARXls(). I can perform forecasts using forecast() with no problems, but when I try to use simulate() with the same model, I get the following error: Error in diag(Cov, p) : 'nrow' or 'ncol' cannot be specified when 'x' is a matrix Can anyone shed some light on the meaning of this error? How can I

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents

I am having problems with the package dse. I just installed R 2.2.1 and reinstalled all packages. I am running Windows XP Pro with all updates. Below there are two examples of error messages generated when trying to execute some simple programs. The code was taken directly from the package documentation. Any help on this will be greatly appreciated. Merry Christmas Fernando

I would like to request help with the installation of dse in raqua in mac os x 10.3. I get the following error message after the messages indicating that parts were successfully installed. I would be most grateful for a solution. ----------------------------------------- * Installing *source* package 'setRNG' ... ** R ** inst ** help >>> Building/Updating help pages for

Hello, I am trying to carry out a phylogenetic autoregression to test whether my data show a phylogenetic signal, but I keep calculating bizzare R-squared values. My script is: > library(ape) > x <-

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, In order to produce an autoregression where only certain lags are allowed, specified in advance (e.g. c(1,2,5) ), I have found it necessary to look beyond the standard [ar] function, thankfully discovering the [FitAR] package, wherein the [FitARp] function provided exactly that capability. However for my problem at hand, [FitARp] is vastly slower than [ar] - taking hours rather than minutes.

This might be obvious to some, but I can't find a neat way to do it: Say I have two (very long) numerical vectors a & b of the same length representing variable coefficients of a linear autoregression. I want to calculate vector x defined by x[1] <- b[1] for (n in 2:length(a)) x[n] <- a[n]*x[n-1] + b[n] Is there a way to do this vectorially, i.e. without using the 'for'

Is there any R package that implements a bifurcating autoregression, aka the BAR(n) model? I've been reading the Huggins and Staudte paper, "Variance Components Models for Dependent Cell Populations", from the Journal of the American Statistical Association, 1994. Shawn Garbett <shawn.p.garbett at vanderbilt.edu> Vanderbilt Cancer Biology 220 Pierce Ave, PRB 715AA

Hi, I am new to R and looking to do auto-regression / ARIMA type modeling. My data has both date and time which I need to combine into a single date-Time value. The time steps are unequal. What package is best for doing the regression and plotting the predicted values against the actual data? Also, what format does my data need to be in when I use the package? For example, I looked at the

Hi there, I ran into some trouble trying to install the dse library on os 10.3 with RAqua as the installation of the dse1 package failed. On the R console I got the error message Warning message: Installation of package dse had non-zero exit status in: install.packages(ui.pkgs, CRAN = getOption(where), lib = .libPaths()[1]) > and the console of the os x said gcc -bundle -flat_namespace

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u