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How can I make sure the residual signal, after subtracting the trend extracted through some technique, is actually trend-free ? I would greatly appreciate any suggestion about some Stationarity tests. I'd like to make sure I have got the difference between ACF and PACF right. In the following I am citing some definitions. I would appreciate your thoughts. ACF(k) estimates the correlation

Hi there, Does anyone know how to predict ARMA? It doesn?t have either predict or forecast methods. I found couple of packages called fbasic and fseries at http://www.itp.phys.ethz.ch/econophysics/R/, which has ?arma.predict? in it, but it doesn?t seem to be working. Any help in this regard would be appreciated. Thanks in advance. Regards Skanda Kallur "Prediction is very difficult,

Dear all R users, I am really struggling to determine the most appropriate lag order of ARIMA model. My understanding is that, as for MA [q] model the auto correlation coeff vanishes after q lag, it says the MA order of a ARIMA model, and for a AR[p] model partial autocorrelation vanishes after p lags it helps to determine the AR lag. And most appropriate model choosed by this argument gives

Dear R-helpers, I have just loaded the fSeries package and I wanted to run the example provided in the documentation of garchOxFit but I got the following: > library(fSeries) > ?garchOxFit > library(datasets) > ?garchOxFit > ## Not run: > ## garchOxFit - > # Load Benchmark Data Set: > data(dem2gbp) > x = dem2gbp[, 1] >

Hello, I am having some problems with fitting an ARMA model to my time series data (randomly generated numbers). The thing is I have tried many packages [tseries, fseries, FitARMA etc.] and all of them giving very different results. I would appreciate if someone could post here what the best package is for my purpose. Also, after having done the fitting, I would like to check for the model's

Hi all, I have some residuals from regression, and i suspect they have correlations in them... I am willing to cast the correlation into a ARMA(p, q) framework, what's the best way to identify the most suitable p, and q, and fit ARMA(p, q) model and then correct for the correlations in regression? I know there are functions in R, I have used them before, but I just want to see if I can do

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

I did a regression with ARMA errors using arima0 with ari<-arima0(y,order=c(2,0,2),xreg=reg1,delta=-1) or ari<-arima0(y,order=c(2,0,2),xreg=reg1) where reg1 is the matrix of the regressors and when I see diag(ari$var.coef) I get negative terms. Do you know what this mean ? I try to change transform.pars to 0 or 1 but this crash R on Windows. Is it possible to test the significativity

I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok

Hi, I am using 2 times series and I want to carry out a regression of Seri1 by Serie2 using structured (autocorrelated) errors. (Equivalent to the autoreg function in SAS) I found the function gls (package nlme) and I made: gls_mens<-gls(mening_s_des~dataATB, correlation = corAR1()) My problem is that I don’t want a AR(1) structure but ARMA(n,p) but the execution fails :

Dear list, according to the documentation of acf{ts} "the partial correlation coefficient is estimated by fitting autoregressive models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Hi R users! I've come across using kpss tests for time series analysis and i have a question that troubles me since i don't have much experience with time series and the mathematical part underlining it. x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287,

Dear all R user, Please forgive me if my problem is too simple. Actually my problem is basically Statistical rather directly R related. Suppose I have return series ret with mean zero. And I want to fit a Garch(1,1) on this. my is r[t] = h[i]*z[t] h[t] = w + alpha*r[t-1]^2 + beta*h[t-1] I want to estimate the three parameters here; the R syntax is as follows: #

Dear R People: I'm trying to use the garchFit function from the library(fSeries) However, R freezes every time that I use it. Is anyone else having this problem, please? Thanks in advance! R Version 2.2.1 Windows. Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: hodgess at gator.uhd.edu

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

I'd like to fit an ARMA(1,1) model to some data (Federal Reserve Bank interest rates) that looks like: ... 30JUN2006, 5.05 03JUL2006, 5.25 04JUL2006, N &lt;---- here! 05JUL2006, 5.25 ... One problem is that holidays have that "N" for their data. As a test, I tried fitting ARMA(1,1) with and without the holidays deleted. In other words, I fit the above data

Hello again! Is there a way to include a mean in the garch function in the library(tseries), please? I tried include.mean=T in the function statement but it didn't work thanks in advance! R Version 2.2.1 Windows Sincerely, Erin mailto: hodgess at gator.uhd.edu

Hello, I am getting the following error message from unitrootTest. Do you have any clue of what could be wrong. Details: AMD64 (x86_64) Gentoo Linux system. library(fSeries) kmodel <- list(ar=c(.3,0,0,0,0.7,-.4*.7),d=1) x=armaSim(nobs,model=kmodel) unitrootTest(x,trend="c",statistic="t",method="adf",lags=2) Error in file(file, "r") : unable to open

Dear R users, I need to fit an ARMA model. As far as I've seen, EACF (extended ACF) is not available in R. 1. Let's say I fit a series of ARMA models in a loop. Given the code/output included below, how do I pull 'Model' and 'Fit' (AIC) from each summary() so that I can combine them into an array/data frame to be sorted by AIC? 2. Apart from EACF, are you aware perhaps