similar to: How to predict ARMA models?

Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

On Tue, Oct 13, 2009 at 5:06 PM, Rolf Turner <r.turner@auckland.ac.nz>wrote: > > Not clear to me what the OP really wants. Perhaps the seasonal > model is what's required; perhaps an arima(1,0,4) model with > theta_2 and theta_3 constrained to be 0. The latter can be > achieved with > > arima(x,order=c(1,0,4),fixed=c(NA,NA,0,0,NA,NA)) > > Or perhaps

Hello, I am running an ARMA model to run forecast for changes in S&P 500 prices. My ARMA calculations look as follows armacal <- arma( spdata, order = c(0,4), lag = list(ma = c(1,2,4)) ) Output: Call: arma(x = spdata, order = c(0, 4), lag = list(ma = c(1, 2, 4)) ) Coefficient(s): ma1 ma2 ma4 intercept -0.073868 0.058020 -0.081292 0.007082 All's

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

Hello, I'm running a number of arima models using the "arima" function. Often, when lag length gets too high, these model don't converge and an error message appears as this: > reg <- arima(y,order=c(7,0,7),xreg=isr) Warning message: In arima(y, order = c(7, 0, 7), xreg = isr) : possible convergence problem: optim gave code=1 In this case, when you print the results

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: #

Hello I am trying to use predict from an arma-Garch model (arma(2, 2) + garch(1, 1)) and I am getting the following error: Error en arima(x = object@data, order = c(max(u, 1), 0, max(v, 1)), init = c(ar, : non-stationary AR part from CSS Does anybody know what can be the reason of this error? The model I have estimated is the following: Title: GARCH Modelling Call: garchFit(formula =

Hello. I am using fGarch to estimate the following model: Call: garchFit(formula = fmla, data = X[, i], trace = F) Mean and Variance Equation: data ~ arma(1, 1) + garch(1, 1) Conditional Distribution: norm Coefficient(s): mu ar1 ma1 omega alpha1 beta1 -0.94934 1.00000 -0.23211 54.06402 0.45709 0.61738 Std. Errors: based on Hessian Error Analysis:

When I do ARMA(2,2) using one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std.

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

Hi everyone, I am performing the time series regression analysis on a series of data sets. A few data sets followed an ARMA(1,1) process. However, they all had a same value of moving average MA coefficients = -1, constantly, from output of function “arima" . Example: > arima(residuals, order=c(1,0,1)) Call: arima(residuals, order = c(1, 0, 1)) Coefficients:          ar1      ma1  intercept

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

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

Hello, I'm an R newbie. I've tried to search, but my search skills don't seem up to finding what I need. (Maybe I don't know the correct terms?) I need the standard errors and not the confidence intervals from an ARIMA fit. I can get fits: > coef(test) ar1 ma1 intercept time(TempVector) - 1900

I am puzzled by the warning message in the output below. It appears whether or not I fit the seasonal term (but the precise point of doing this was to fit what is effectively a second seasonal term). Is there some deep reason why AR parameters ("Warning message: some AR parameters were fixed: ...") should somehow intrude into the fitting of a model that has only MA terms? >

In the summary of the output of arma, there's a number Pr(>|t|), however, I don't know what is its meaning - at least, it doesn't _seem_ to be a Student's t distribution. Reproducible test case: x <- c(0.5, sin(1:9)) reg <- arma(x, c(1,0)) summary(reg) <output> Call: arma(x = x, order = c(1, 0)) Model: ARMA(1,0) Residuals: Min 1Q Median 3Q

Dear list and Martin, I'm testing different approaches to fit an electricity demand time series and come upon the fracdiff package (v 1.3-1) for fitting fractional ARIMA models. The following questions are motivated by this package. 1. Despite having a help page, the residuals and fitted functions don't seem to have implementation, or did i miss something obvious? Alternatively, having a

I can get an array of strings for the data that I want using 'paste()' as follows: paste('ma', 1:am$arma[2], '=', coef(am)[1:am$arma[2] + am$arma[1]], sep='') This results in a vector of strings like: [1] "ma1=1.17760133668255" "ma2=0.649795570407939" "ma3=0.329456750858276" What I would like is fixed.pars <-

Dear R People: I have the output from an arima model fit in an object xxx. I want to verify that the ma1 coefficient is there, so I did the following: > xxx$coef ar1 ar2 ma1 intercept 1.3841297 -0.4985667 -0.9999996 -0.1091657 > str(xxx$coef) Named num [1:4] 1.384 -0.499 -1 -0.109 - attr(*, "names")= chr [1:4] "ar1" "ar2"