similar to: RE: GARCH (1 , 1), Hill estimator of alpha, Pareto estimator]

Dear R users, Could you please help me out. I am in trouble as I am unable to model graphs to explain the GARCH (1 , 1) model, the Hill estimator (of alpha), and the Pareto estimator. I just got introduce to R. I am working on a paper which must be worked from R. You look at the difficulty I had from the text below. [1] "DAX" "DAX_CAC" "DAX_CAC40"

Hello, My problem is, that I have to build hundreds of GARCH models to obtain volatility forecasts. I would like to run a loop, that would build those forecasts for me. There is no problem, with writing only the results of the forecasts, but I'd like to have stored results of the models in some file, that I could check later, what are the models like, to be able to compare if I should use

Hi Everybody, I am a total beginner at this so please bear with me. I downloaded by hand the file WIG20.txt (Warsaw Stock Exchange Index of 20 most important stocks). The format is this: Name,Date,Open,High,Low,Close,Volume WIG20,19940414,1000.00,1000.00,1000.00,1000.00,71600.000 WIG20,19940418,1050.50,1050.50,1050.50,1050.50,99950.000 WIG20,19940419,1124.90,1124.90,1124.90,1124.90,138059.000

Hi all, I am trying to find the autocorrelation of some time series. I have say 100 files, some files have only missing values(-99.99, say). I dont want to exclude these files as they represent some points in a grid. But when the acf command is issued i get an error. Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In min(x) : no

I installed this game on Wine 1.0 RC and runs. But graphic is garbage: [Image: http://img233.imageshack.us/img233/4451/zrzutekranudefaultwinedwi0.th.png ] (http://img233.imageshack.us/my.php?image=zrzutekranudefaultwinedwi0.png) [Image: http://img181.imageshack.us/img181/7017/zrzutekranudefaultwinedqs8.th.png ] (http://img181.imageshack.us/my.php?image=zrzutekranudefaultwinedqs8.png) When I try

Hi all, I have just uploaded the hbim package. This package does calculations related to the Hill/Bliss independence model as applied to mulitcomponent vaccines. Specifically, the package caculates expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. It uses Hill models for each component which are combined

Hi all, I have just uploaded the hbim package. This package does calculations related to the Hill/Bliss independence model as applied to mulitcomponent vaccines. Specifically, the package caculates expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. It uses Hill models for each component which are combined

I have this error why the reason for that? [Image: http://s3.subirimagenes.com:81/imagen/previo/thump_6438221pantallazo.png ] and when i try install visual c++ i get this error [Image: http://s2.subirimagenes.com/imagen/previo/thump_6438228pantallazo1.png ] says i cant access for service of windoes installer this error made for not correctly installa or i'm log in in safe mode. Help me

Dear all, Recently I am generating large random samples (10M) and any duplicated numbers are not desired. We tried several RNGs in R and found Wichmann-Hill did not produce duplications. The duplication problem is the interesting birthday problem. If there are M possible numbers, randomly draw N numbers from them, the average number of dupilcations D = N(N-1)/2/M. For Knuth-TAOCP and

Hello all, I am trying to estimate the cumulative distribution function for a single stock return time series. A piecewise estimation is composed of three parts: parametric generalized Pareto (GP) for the lower tail (10% of the observation), non-parametric kernel-smoothed interior (80% of the observations), and GP for the upper tail (10%). I wonder if anyone has clue about this in R. The

Dear All, I am trying to fit some data to a Pareto distribution and would like to estimate the parameters with the fitting. I have come across some options so far. Unfortunately I haven't managed to get any of them to make the right fits (as is evident when I check with the goodness of fit). One such option is: library(VGAM) b1 <- read.table(file("FitPareto_Values.txt",

I would like to fit a Pareto Distribution and I am using the following codes. First, I thought the fitted (fit1) should be the fitted value for the data, is it correct? As the result of the "fitted" turns out to be the same value. fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c") coef(fit, matrix=TRUE) summary(fit) fitted(fit) Secondly, how can I plot the

Hi, guys, I need to simulate Pareto distribution. But I found 'rpareto' didn't exist in R. And it seems that Pareto distribution don't have mathematical relationships with other distributions. What can I do? Thanks a lot. Ni --------------------------------- [[alternative HTML version deleted]]

Dear R-users, I am trying to fit pareto distribution to some data but i've one problem. Using optim to calculate the maximum of the likelihood function of the pareto I use as start parameters the moments method(using the distribution function in the package actuar): media=mean(x) var=mean(x^2)-media^2 scale=2*var/(var-media^2) shape=(scale-1)*media

Dear R users, the first 1.0 version of the rPref package is now on CRAN. rPref allows to select the Pareto-optimal tuples from a data set, also called Skylines in the database community. For example, optimal tuples from mtcars according to "high(mpg) * high(hp)" (where "*" is the Pareto operator) are those cars, for which no other dominating car exists. There, a car

Dear all: First, apologies for cross-posting multiplicities and for a query that is more analytically related than S-language related. The bottom-line wish is: Could you please provide and advice, references, etc on S software approaches for fitting a distribution with density: p*g(x) + (1-p)*f(x) where g(x) is the familiar lognormal 2-parameter density and f(x) is Pareto as defined below?

Hi, I'm fitting a generalized Pareto distribution to POT exceedances of a data set. The practical stuff works ok, but I have a question regarding theory. Is there an equation relating parameters of a gpd tail to its (first) moments? According to theory for certain parameters either the first moment does not exist or the distribution has an upper bound, but I haven't found the

I would like to fit a Pareto Distribution and I am using the following codes. I thought the fitted (fit1) should be the fitted value for the data, is it correct? As the result of the "fitted" turns out to be a single value for all. fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c") fitted(fit) The result is fitted(fit) [,1] [1,] 0.07752694

I would like to fit a Pareto Distribution and I am using the following codes fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c") fitted(fit) But the fitted values turn out to be the same for each observation. I guess the problem is with "ycf1 ~ 1", I would be grateful if anyone can give me some advice on how to define the formula. Many thanks -- View this

Hi, I would like to use the following codes to plot the CDF for pareto distribution. Before doing this, I have plot the emperical one. x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4477557,), col="red") Could anyone give me some advice whether the above codes are correct? Many thanks. -- View this message in context: