similar to: ARMAtoMA

Hello R Users! I have a question about the output of ARMAtoMA when used to calculate the variance of a model. I have a mixed model of the form ARMA(1,1). The actual model takes the form: X(t) = 0.75X(t-12) + a(t) - 0.4a(t-1) Given that gamma(0) takes the form [(1 + theta^2 - 2*theta*phi)/(1-phi^2)]*sigma(a), I would expect a process variance of 4.02*sigma(a) when I substitute 0.75 for phi and

Full_Name: Madeleine Yeh Version: 1.9.1 OS: AIX 5.2 Submission from: (NULL) (151.121.225.1) After compiling R-1.9.1 on AIX 5.2 using the IBM cc compiler, I ran the checks. One of them failed. Here is the output from running the check solo. root@svweb:/fsapps/test/build/R/1.9.1/R-1.9.1/tests/Examples: ># ../../bin/R --vanilla < stats-Ex.R R : Copyright 2004, The R

Thanks a lot to everybody that helped me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed out and error catched so that it doesn't halt parent loops to search for

Hi, I am trying to write the Realized GARCH model with order (1,1) The model can be describe bellow: r_t = sqrt( h_t) * z_t logh_t = w + b*logh_(t-1) + r*logx_(t-1) logx_t = c + q*logh_t + t1*z_t +t2*(z_t ^2 -1) + u_t and z follow N(0,1) , u follow N(0, sigma.u^2) But I'm troubled with the simulation check for my code. After I simulate data from the model and estimate the data, I

Hi , R is a new language for me so sorry in advance if this error is to basic for posting. I have tried the R manual and search online for quite a few, if anyone could help i would be very thankful. Here is my code. kappa = 1.1 theta = 0.1 sigma = 0.4 rho = -0.6 v0 = 0.2 r = 0.05 T = 0.5 s0 = 1 K = 0.5 type = 1 Hestoncall = function(kappa,theta,sigma,rho,v0,r,T,s0,K,type) { u = 0.5 b

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

Dear Sir, I think I found a bug in the function arma.sim, which is defined in the help page of the function filter: arma.sim <- function(n, ar = NULL, ma = NULL, sigma = 1.0) { x <- ts(rnorm(n+100, 0, sigma^2), start = -99) if(length(ma)) x <- filter(x, ma, sides=1) if(length(ar)) x <- filter(x, ar, method="recursive") as.ts(x[-(1:100)]) } I am using R

Hello expeRts, I need generate symbolize the autocovariances matrix of a Gaussian ARMA(1,1), for derivate it and evaluate. I try this codes, but whitout sucess vacv<-NULL vacv[1]<-1-2*phi*theta-theta^2 vacv[2]<-(1-phi*theta)*(phi-theta) vacv[3:n]<-acv[2]*(phi^(1:(n-2))) facv<-list() for(i in 1:2)

Hi all, I'm trying to write a MH algorithm in R for a standard normal distribution, I've been trying for a good week or so now with multiple attempts and have finally given up trying to do it on my own as I'm beginning to run out of time for this, would somebody please tell me what is wrong with my latest attempt: n=100 mu=0 sigma=1 lik<-function(theta) exp(((theta-mu)^2)/2*sigma)

Hi: I am trying to run your SCHOOLS example as following and I get an error message stated that : Error: could not find function "bugs" Would you please help me since I would like to run some WINBUG programs from your R by reading external data and I am new to R? Do I missing some function? Should I install some extra files? Thanks a lot in advance for your help!! > schools

Thanks to Gabor for setting me right. My code is as follows. I found it useful for learning optim(), and you might find it similarly useful. I will be most grateful if you can guide me on how to do this better. Should one be using optim() or stats4::mle? set.seed(101) # For replicability # Setup problem X <- cbind(1, runif(100)) theta.true <- c(2,3,1) y <- X

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

Hi, I got the following problem. I am doing a maximum likelihood estimation for a Kalman Filter. For this purpose, I have to invert an error matrix Ffast of dimension "no. parameters X no.parameters". The usualy optim methods often find only local minima, so I decided to make the optimization using the SANN algorithm, which is very slow already. However, this becomes a real problem

http://r.789695.n4.nabble.com/file/n4619404/pic1.jpg cesres_ext <- nls(lnGDP85~ intercept + (alpha/(1-alpha-beta)) * lns_ikonngdelta + (beta/(1-alpha-beta)) * lns_ihonngdelta + 0.5 * ((sigma-1)/sigma) * (1/((1-alpha-beta)*(1-alpha-beta))) * (alpha * taylor1 + beta * taylor2 - alpha*beta*taylor3) ,start = list(intercept=8, alpha=0.2, beta=0.4, sigma=1.2),data=data) I have this model. I use

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

Hello, I am trying to estimate parameters of mean reverting process with jumps given by: dp=k(mu-p)dt+sigma*dz+Jdq where dp represents change in log of price, k is reversion factor, mu is long run level of price, sigma is standard deviation, and dq equals one with probability lambda if jump occurs and 1-lambda otherwise. J is jump size with mean muj and standard deviation delta. Therefore, when

Dear R-users, I am trying to use the following code to reproduce results from Prof. Gelman's book, but have the listed error for R2WinBUGS version (the openbugs version is good). I am using R-2.6.1 on windows XP, and all the R packages are most current ones. schools.bug can be found at http://www.stat.columbia.edu/~gelman/bugsR/runningbugs.html . Can anyone help me to figure out what's

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

This message is about a curious difference in timing between two ways of computing the same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to be a factor of >1000. The code is below. We would be grateful if anyone can point out any egregious bad practice in our code, or enlighten us on why one approach is so much slower than the other. The problem

Hello list, I?ve been trying to get OpenBUGS running on my mac using the wine emulator. I can run Openbugs just fine by doing: wine ~/OpenBUGS312/OpenBUGS.exe In the terminal, so OpenBUGS works. When I try to run the schools example using rbugs(), the OpenBUGS process starts in wine, but it just sits there, no log, no script, no output of any sort. The rbugs () call makes the init,