similar to: model of fish over exploitation

Dear Users, I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the innovations are supposed to follow a t-GARCH(1,1) proccess. By t-GARCH I want to mean that: e_t=n_t*sqrt(h_t) and h_t=ct2+a*(e_t)^2+b*h_t-1. where n_t is a random variable with t-Student distribution. If someone could give some guidelines, I can going developing the model. I did it in matlab, but the loops

Hey, I'm trying to implement a GARCH model with Johnson-Su innovations in order to simulate returns of financial asset. The model should look like this: r_t = alpha + lambda*sqrt(h_t) + sqrt(h_t)*epsilon_t h_t = alpha0 + alpha1*epsilon_(t-1)^2 + beta1 * h_(t-1). Alpha refers to a risk-free return, lambda to the risk-premium. I've implemented it like this: #specification of the model

Folks, S_t = (x_t, y_t) is the state of a system at time t. There is an iterative function which converts S_t into S_{t+1}. I can easily write this in R like this: iterate <- function(S) { list(S$x+1, S$y+1) } So this function eats S_t and makes S_{t+1} and I can say S2 <- iterate(S1) My question: suppose I want to iterate from 1..10, what is the data structure

Hi all, Today, i just installed the newest R version 2.10.1 and other necessary tools for building R package under windows,e.g. Rtools, perl. All are the newest version. After the correct configuration under windows, i use it to re-check my old package. I found the following prolem when checking EXAMPLE, which did not exist before this re-installation. ######## * checking examples ... ERROR

I am using the following code: library(lattice) data<-read.csv("data.csv") attach(data) fig<-xyplot(S_t~month|event, key = list(text=list(lab=c("Time to first CV event - Data", "Survival post first CV event - Model", "Survival post first MIA/CA event - Data",

Hi all, Here the first bunch of patch for compiling part of LLVM under win32 with MSVC 7.1: * Trivial addings (I hope!): - #include <string> at top of: llvm\include\llvm\ExecutionEngine\ExecutionEngine.h(78) : error C2039: 'string' : is not a member of '_STL' - #include <algorithm> at top of: llvm\lib\CodeGen\LiveIntervalAnalysis.cpp(639) : error C2039:

Hi, Does R have any functions implementing such multinomial regression: (S_t^A,S_t^B)~MN(N_t-Y_{t-1},P_t^A,P_t^B) where MN(n,p_1,p_2) is multinomial distribution with parameters n, p_1, p_2. Here P_t^A and P_t^B are nonlinear functions from predictor variables and parameters which need to be estimated. Here A and B are used for notation, they are not parameters. My second question is about

---------- Forwarded message ---------- From: ma yuchao <ma.yuchao@gmail.com> Date: 2006-5-20 ÉÏÎç4:01 Subject: hello, everyone To: R-help@stat.math.ethz.ch Hello, R people: I have a question in using fSeries package--the funciton garchFit and garchOxFit if adding a regression to the mean formula, how to estimate the model in R? using garchFit or garchOxFit? For example,

I'm looking at what would be involved in converting from MeetMe to ConfBridge and there seems to be a lot of missing administrative things, but I hope I'm just missing it. We all know about the missing realtime linkage. That's a major nuisance, but can be worked around. More serious is that the CLI command to display users in a ConfBridge don't show the caller ID information, so

-- View this message in context: http://r.789695.n4.nabble.com/Generalized-DCC-GARCH-ML-estimation-tp2245125p2245125.html Sent from the R help mailing list archive at Nabble.com.

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

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

Hi All, I'm trying to do a Scatterplot (package: car), and add a line (just for reference). There is my code: #------------------------------------Code--------------------------------------------------- library("car") library("calibrate") G_T<-c("car","bike","boat") ave<-c(80,10,45) perf<-c(100,80,75) df2<-data.frame(G_T,ave,perf)

Dear r-users! I have another question regarding the dlm package and I would be very happy if someone could give me a hint! I am using the dlm package to get estimates for an endogenous rate of capacity utilization over time. The general form of a state space model is (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) The

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

Hi, I've attached two files with the sources for a function to implement the finite difference method for solving a particular differential equation. One of them works correctly, the other gives wrong results or returns an error, depending on the version of R. The difference between them is that in the 'broken' version in line 42 I check if the items in the two-dimensional array

I am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

Hi, I am trying to figure how to use dlm with constant terms (possibly time-dependent) added to both equations y_t = c_t + F_t\theta_t + v_t \theta_t = d_t + G_t\theta_{t-1} + w_t, in the way that S-PLUS Finmetrics does? Is there any straightforward way to transform the above to the default setup? Thanks, Tsvetan -------------------------------------------------------- NOTICE: If received in