similar to: why this function give error message

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at

Hello, i want to estimate a complex GARCH-model (see below). http://r.789695.n4.nabble.com/file/n4112396/GJR_Garch.png W stands for the Day of the Week Dummies. r stands for returns of stock market indices. I stands for the GJR-term. I need some help with three problems: 1.) implementation of the GJR-term in the variance equation 2.) compute robust covariance matrix

Hi, Could you please help me with EGARCH and GJR? Is it possible to use EGARCH and GJR in R? I have used below mentioned code for GARCH in R, but I never used EGARCH and GJR in R. Thank you in advance! daten<-read.table("H://Daten//Zeitreihen//dax_1.csv", sep=";", header=T) DAX.kurs<-daten DAX.kurs<-ts(DAX.kurs,names="DAX-Kurs")

Good day Ma'am/Sir, I am Resa Mae R. Sangco a Master of Statistics student from the MSU- Iligan Institute of Technology located in Iligan City, Philippines. I am currently doing my thesis entitled ?Bayesian Analysis in GJR-GARCH (p,d) model with Student-t innovations". In finding my posterior distribution since it is hard to integrate, I use Markov Chain Monte Carlo simulation

Dear All, Sorry to bother I want to write a function in R using if Say I have a dataset x, if x[i]<0, then x[i]=x[i], if x[i]>0, then x[i]=0 for example, x=-3:3, then using the function, x becomes [-3,-2,-1,0,0,0,0] I write the codes as follows, gjr=function(x) {lena=length(x) for(i in 1:lenx) if (x[i]<0) return (x[i]) if (x[i]>0) return (0) x} but then, doing gjr(x? it only

Dear all, I would like to know that R has the function for garch-t,gjr- garch,qgarch and egarch. Best Regards, Luck

hi, I try to code garch-t(1,1),egach(1,1) and gjr(1,1) to estimate my data. How I can code these model with my data (e.g. garch code is y<-garch(x,order=c(1,1)) best regards, luck

Dear R-helpers, I was wondering if anyone has or knows someone who might have an implementation of algorithm for estimating garcht-t, egarch and gjr models. I try to use Fseries but I don't know how to code these models. Thanks a million in advance, Sincerely, Nongluck

Hello all, I would like to know how to call sas code in R. Since I simulate data in R and I need to use sas code (garch-t,egarch and gjr) to estimate it. I need to simulate 500 times with 2000 obs. How I can call that code in R.Also, how I can keep the parameters from the estimate. j=1:500 i=1:2000 sas code keep parameters. Best Appreciate, Luck

Hi there, I'm trying to fit a GJR-GARCH Model using fGarch. I wanted to try that by fitting an APARCH model with a fixed delta of 2 and a non-fixed gamma. So I was simply trying to use: spec <- garchFit(~aparch(1,1),data=garchSim(),delta=2) coef(spec) And sometimes, it's working like a charm and delta is indeed exactly 2 in the resulting coefficient vector. Frequently, though, the

dear list, as a part my problem. I have to estimate some parameters using ML estimation. The form of the likelihood function is not straight forward and I had to use a for loop to define the function. I used "optim" to maximise the result but was not sure of the programme. To validate my results, I tried to write a function to obtain the MLE of a bivariate normal in the same manner. On

HI, I am using Windows XP and R version 2.9.2. I have a data frame written by R similar to the following: Lab_ID Analysis_Soil Results -4MAD -2.5MAD +2.5MAD +4MAD 55003 Calcium-2008-116 900 961 1121.5 1656.5 1817 55003 Calcium-2008-117 3321 2175 2380.5 3065.5 3271 55003

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Dear R-help, I'm using the 'optim' functions to minimise functions, and have read the documentation, but I'm still not sure how to determine sensible values to use for the 'fnscale' and 'parscale' options. If I have understood everything correctly, 'fnscale' should be used to scale the objective function, so that for example if the default is

I'm using R 2.0.1 under Windows XP. I get the following message when I run the code listed below. I don't seem to have any problems using the function "slice" outside "integrate". Error in integrate(slice, 0, Inf, , , , , , , a, b) * delta : non-numeric argument to binary operator [ By the way, I'm trying to evaluate a two-dimensional integral by

I'm having trouble with the standard deviation distribution as shown on http://mathworld.wolfram.com/StandardDeviationDistribution.html . (Eric Weisstein references Kenney and Keeping 1951, which I can't check.) I believe the graphs they show, but when I code the function in R, according to the listed formula, I get very different graphs. Would someone please point out my error or tell

Would anyone be able to provide insight for the following question, please? Setting: estimation of prediction intervals for age-period-cohort models using GAMs (rate ~ s(age,period)) Method: bootstrap (Davison and Hinkley, 1997) Issue: standardisation of the residuals for resampling requires an adjustment using the diagonals of the hat matrix. Is there a simple way to get the hat values out of a

Dear knowing people, Dennis Murphy helped me a lot with my first loop last week. Thanks again - I could have made more than 10 "Thank-You cakes" in the time it saved me! But now I want to complicate the thing. My ideas didn't work. Let's see if anyone is smarter ;-) The following packages are needed: library(adehabitatHR) library(rgdal) library(plyr) # My dataframe looks

Estimados una consulta me encuentro graficando un histograma cuyos datos no provienen de una distribución clásica como la normal exponenial, poisson, etc, Lo que necesito es colocar una curva no paramétrica que permita evidenciar el ajuste de los datos a esa curva ya que son muchos (alrededor de 80000). Muchas gracias [[alternative HTML version deleted]]

Hi Dimitri, Thanks for help, i knew that way that you have suggested but in fact my case is a little bit complicated than this, therefore, i am writing a better explanation now, my model is trying to see the effect of eco-innovative industries on total output, thats why i am trying to estimate by-industry regression: the model is lnQ~lnC+lnM+lnL+lnE+eco_inno+inno+(sum)ind_3d capital,material