similar to: generating time series data

Dear all, I need to compute the ACF (autocorrel) of an AR6 process, given the values of its parameters (w1,w2,w3,w4,w5,w6). First, I notice that there is an error as soon as the sum of the wi equals 1 : "Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number = 1.00757e-18"

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 :

Hi, I am new to using R for Time series analysis. I was wondering if there are any functions that can convert ARMA or ARIMA time series into their corresponding AR or MA time series representations (by calculating the corresponding AR or MA coefficients). Thanks a lot Kris.

hi, in 1.9.1, the return value from ARMAacf(pacf=TRUE) is not named by lags, contrary to ?ARMAacf. the simple fix is to move names(Acf) <- down after if(pacf), with an appropriate starting lag as pacf=TRUE appears to start at lag 1 (whereas pacf=FALSE starts at lag 0). for consistency, one could argue to append 1 for lag 0 for pacf=TRUE (or start pacf=F at lag 1). however, given the

Time Series functions in R ========================== I think a good basic S-like functionality for library(ts) in base R would include ts class, tsp, is.ts, as.ts plot methods start end window frequency cycle deltat lag diff aggregate filter spectrum, spec.pgram, spec.taper, cumulative periodogram, spec.ar? ar -- at least univariate by Yule-Walker arima -- sim, filter, mle, diag, forecast

i have attached time series file http://r.789695.n4.nabble.com/file/n4521545/1A1I_A_phi_psi_pot_r_k.txt 1A1I_A_phi_psi_pot_r_k.txt i am not understanding formula() function,but what i read,from that 1 st method:- > t<-read.table(file.choose()) #choosing attached file > lm<-lm(t$V1~ 1) # i used Tilde sign > loglik<-logLik(lm) > AIC(loglik)

Hi Everyone, I am trying to do a time series regression using the lm function. However, according to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to use in the gls function? Or is there any other way to do the time series regression in R? I highly

i am new to time series,whatever i know up till now,from that i have uploaded time series file & what to build arma model,but for that i want p & q values(orders) tell me how to calculate best p & q values to find best AIC values for model i am doing but giving error >bhavar<-read.table(file.choose()) #taking time series file > decompose(bhavar$V1) Error in

I've been looking for an IACF() procedure in R for a long time (it's a very convenient function to check for overdifferencing time series), and eventually decided to write my own function. Here's what I came up with : 3 web-pages helped me estimate it : http://www.xycoon.com/inverse_autocorrelations.htm

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

Buenos días, estoy indagando un poco a fondo las series de tiempo y quisiera saber que paquete me recomienda acerca de este tema. Muchas Gracias. Wilme Contreras. [[alternative HTML version deleted]]

Hi all, Please recommend good books for the following three categories. (I am aim at finance, macroeconomics, trading and business applications). (1) statistical (financial) data analysis; (2) time series; (3) econometrics. More specifically, I am looking for the following two types of books: (1) Books that provide big pictures and intuitions and books that connect dots... For example, there

Dear all, I found really difficult with the time series questions, please help me with this monthly airline series! I have run the following r code, and there is an error appeared at the end. The data files was enclosed in the email. I'm sorry the errors message appeared in chinese, but it says "plot.xy(xy.coords(x, y), type = type, ...) : errors in argument has more than 3

Hello, I have time series and need to draw simple and partial correlograms with associated Q-statistics (the same as in EViews). Can I do it in R? Thanks --------------------------------- [[alternate HTML version deleted]]

Hi, Could anyone inform how to perform multi-variate auto regression using the past 't' values for regression in R. I have looked at ARMA provided by DES library and mvr provided by PLS library but could not match them to my requirements. Specifically, I want the following Say I have attributes a1-a4. and the regression equation is as follows: a4(t) =

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),

Hi everyone, Currently I work for Master degree in statistics at Garyounis University. My dissertation on bootstrapping linear time series models, I mainly use R language, however, I must confess that I find it difficult to bootstrap moving average models and ARMA models , therefore I would be very grateful if you advise me on this matter. Yours

Hi all, I was running the adf test in R. CODE 1: adf.test(data$LOSS) Augmented Dickey-Fuller Test data: data$LOSS Dickey-Fuller = -1.9864, Lag order = 2, p-value = 0.5775 alternative hypothesis: stationary CODE 2: adf.test(diff(diff(data$LOSS))) Augmented Dickey-Fuller Test data: diff(diff(data$LOSS)) Dickey-Fuller = -6.9287, Lag order = 2, p-value = 0.01 alternative

Background: OS: Linux Mandrake 9.1 release: R 1.9.0 editor: Xemacs 21.4 frontend: ESS 5.1.23 --------------------------------- Colleagues I have two time series (upwelling index and water temperature) of evenly spaced, daily data over 18 months, but the upwelling index series has a gap of about 2 months right in the middle of it. I want to do the acf, pacf, ccf, and a cross-spectral analysis

This is a time series\optimization rather than an R question : Suppose I have an ARMA(1,1) with restrictions such that the coefficient on the lagged epsilon_term is related to the coefficient on The lagged z term as below. z_t =[A + beta]*z_t-1 + epsilon_t - A*epsilon_t-1 So, if I don't have a facility for optimizing with this restriction, is it legal to set A to something and then Optimize