similar to: AutoRegression with Subset of Lags/Coefficients

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'

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:

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 <-

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

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

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,

dear All, I have the following problem: I need to calculate an h-step ahead forecast from a var model (estimated with a dse1 method estVARXls), which in turn will be used as an input for another model as conditioning data, so I need it as a simple, numeric matrix. No exogenous input is used. However, the standard forecast method produces a 1-element list that includes a forecast matrix, yet I

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

Hello - I need to do simple linear autoregressive model with R software for my thesis. I looked into all your documentation and I am not able to find anything too helpful. Can someone help me with the codes? Thanks Emil [[alternative HTML version deleted]]

Hey, all, I may just be missing something, but I'm trying to construct a temporal autoregression with an independant variable other than just what is happened at a previous point in time. So, the model structure would be something like y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a*x(t) I'm even considering a model of y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a1*x(t)+a2*x(t-1)... So, my data looks like

Hi all, I got another VAR question here and really appreciate if somebody would help me out :) I have five time series, say A,B,C,D,E. My objective is to predict the series A using the rest, that is, B, C, D and E. A Vector Autoregression Model should work here. But first of all, I should select which series of B, C, D and E to be include in the VAR model, as well as the number of lags. I wonder

I have not been able to find any programs for running vector autoregressions with R. I am interested in running Bayesian VARs and also running VARs that run all combinations of variables in the vector. Is anyone currently developing this? -Nirav Mehta

Dear R users, I would like to simulate, for 20000 replications, an autoregressive process: y(t)=0.8*y(t-1)+e(t) where e(t) is i.i.d.(0,sigma*sigma), Thank you in advance ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

I noticed that when i launched WINWORD.EXE the first time it took about 20 seconds to load, but when i ran it on the 2nd time it took about 10 seconds only!, it seems that it is now cached in the memory...I was wodering if it is possibe to load wine as a daemon so that it will be faster to load? sort of like a daemon? does my analogy here make sense? I would appreciate any help, comments,

Hi all i would like to know if there's a software to create a dj station taking the input from mic and stream with icecast, also i need to know if is possibe to mix mic input and ogg file and stream it togheter with icecast thanks __________________________________________________ Do You Yahoo!? Poco spazio e tanto spam? Yahoo! Mail ti protegge dallo spam e ti da tanto spazio gratuito per i

Hello All, I have a linux box running with three NIC''s, I have used brctl tool to configure this box as a bridge, i have given it ONE logical ipaddres, Every thing is working fine, but now i am required to configure the same bridge as a LOAD BALANCER.... On some googling i found the following link http://lartc.org/howto/lartc.rpdb.multiple-links.html This HOWTO requires me to have one

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

R version 2.12.0, 64 bit on Windows. Here is a short script that illustrates the problem: library(tseries) library(xts) setwd('C:\\cygwin\\home\\Ted\\New.Task\\NKs-01-08-12\\NKs\\tests') x = read.table("quotes_h.2.dat", header = FALSE, sep="\t", skip=0) str(x) y <- data.frame(as.POSIXlt(paste(x$V2,substr(x$V4,4,8),sep=" "),format='%Y-%m-%d