similar to: All subsets vector autoregression with exogenous variables

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

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, 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.

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

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

>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

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 <-

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 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

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

Hello, I have a question about doing ALL possible subsets regression with a general linear model. My goal is to produce cumulative Akaike weights for each of 7 predictor variables-to obtain this I need R to: 1. Show me ALL possible subsets, not just the best possible subsets 2. Give me an AIC value for each model (instead of a BIC value). I have tried to do this in library(RcmdrPlugin.HH),

Hi all I am hoping this is just a minor problem, I am trying to implement a best subsets regression procedure on some ecological datasets using the regsubsets function in the leaps package. The dataset contains 43 predictor variables plus the response (logcount) all in a dataframe called environment. I am implementing it as follows: library(leaps)

h?, I am preparing undergraduate thesis If you help me this would make me feel good. First I need to analyze effect of Dow Jones Industrial average(DJIA)'s return on Istanbul Stock Exchange(ISE). I want to use Markov-Switching Bayesian Vector Autoregression Models (MSBVAR) that is used to examine the effect of a large economy?s stock exchange movement on a small economy?s stock exchange

Dear R-users, Is anyone successfully using leaps() and particularly subsets() (contributed function) in R? When I attempt to run them with modest matrices, R promptly faults and dumps the core. Also, how does one use subsets.formula() within subsets()? R : Copyright 1997, Robert Gentleman and Ross Ihaka Version 0.50 Alpha-4 (September 10, 1997) R-base-0.50.a4-1.i386.rpm

Hi, I have found a minor problem with leaps(). In 1.3.1 under Windows 2000 I seem to only be able to obtain values for one statistic at a time. That is choosing method=c("Cp","adjr2","r2") just gives Cp values. To mimic the output of Minitab's MTB > BReg 'Fertility' 'Agriculture'-'Infant.Mortality' ; SUBC> NVars 1 5; SUBC>

Hello List, I was wondering if a package or piece of code exists that will allow all possible subsets regression model selection within program R. I have already looked at step(AIC) which does not test differing combinations of variables within a model as far as I can tell. In addition I tried to use the leaps command, but that does not use the criterion I am looking for. Any help or advice