similar to: where do I find stochastic volatilities models in R or Matlab?

Dear everyone, i`m a german economics student, writing my master´s thesis about "Multivariate Volatility Models". After having read about theoretical aspects of Multivariate GARCH ans Stochastic Volatility Models, I would like to compare DCC-GARCH and DC-SV with help of an empirical application. I figuered out that one has to use MCMC-simulation-methods for that. Some days ago I

Hi all, Could anybody point me to some overview/survey papers about using particle filters and sequential monte carlo methods to estimate stochastic volatilities? I couldn't find any such articles giving a big-picture view of the literature. How do these estimation methods compare to EMM and other Bayesian methods for estimating stochastic volatilities? Also, I am looking for some

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

On the cover of Zivot and Wang's Modeling Financial Time Series with S Plus, there is a correlation plot that seems to indicate the strength of correlation with color-coded squares, so that more highly correlated stocks appear darker red. If anybody out there is familiar with the book or understands what I am talking about, I am curious as to whether or not there is a similar function in R

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

Dear all, I am running matrix population models using package "popbio". In a deterministic model {i.e., transition matrix is defined as A <- matrix(c(0.70, 0.70,0.35,0.50), nrow=2,byrow=TRUE}, population growth rate can be estimated from the dominant eigenvalue {command "eigen.analysis"}. However, I cannot figure out the way to compute the asymptotic stochastic population

Hi. guys, I am trying to write my own Stochastic Gradient Ascent for logistic regression in R. But it seems that I am having convergence problem. Am I doing anything wrong, or just the data is off? Here is my code in R - lbw <- read.table("http://www.biostat.jhsph.edu/~ririzarr/Teaching/754/lbw.dat" , header=TRUE) attach(lbw) lbw[1:2,] low age lwt race smoke ptl ht ui ftv

Dear all, I want to finished my paper by latent class Stochastic Frontier Analysis , but i can not find the package, is there anyone that may help me Thanks a lot. [[alternative HTML version deleted]]

Hi, I am trying to find out the scale efficiency and optimal scale of banks by stochastic frontier analysis given the panel data of bank. I am free to choose any model of stochastic frontier analysis. The only approach I know to work with R is to estimate a translog production function by sfa or other related function in frontier package, and then use the Ray 1998 formula to find the scale

Dear all, S+Finmetrics has a number of very specilised functions. I am particularly interested in the estimation of cointegrated VARs (chapter 12 of Zivot and Wang). In this context the functions coint() and VECM() stand out. I looked at package "dse1", but found no comparable functionality. Are there any other packages you could point me to? In general, are there efforts for

Hello R users, I am trying to calculate the stochastic lambda for a published matrix population model using the popbio package. Unfortunately, I have been unable to match the published results. Can anyone tell me whether this is due to slightly different methods being used, or have I gone wrong somewhere in my code? Could the answer be as simple as comparing deterministic lambdas to

I need to estimate maximum tree crown radius and am looking for a package to prepare stochastic frontier models in R. I have not found any package references on Nabble R help, google, or R help. Any tips on a package for this? With regards, Aaron Trowbridge Researcher BV Research Centre Smithers B.C. -- View this message in context:

Dear all, I want to program my own models about Stochastic Frontier Analysis and Linear programming (Data Envelopment Analysis). In this context, is there anyone that may help me with some simple tutorials and scripts about these issues? Thanks a lot. -- Marcus Vinicius Pereira de Souza, Prof. [[alternative HTML version deleted]]

Dear colleagues, I?m a new R-help user. I?ve read the advertisements about the good manners and I hope to propose a good question. I?m using R to build an epidemiological SEIR model based on ODEs. The odesolve package is very useful to solve deterministic ODE systems but I?d like to perform a stochastic simulation based on Markov chain Montecarlo methods. I don?t know which packages could be

(apologies for the cross-posting, and for this being a more general stats question rather than a specific-to-R one. I assure you I will be doing the actual analysis in R :) I am trying to determine the distribution and variance for a classic stochastic (transition) matrix problem such that: let x(t) be an initial state vector consisting of counts of classes A, B and C: x(t) =

I am interested in creating multiple (say 1000) time series, from a given stochastic process, of length 250. I want to refer to each realization with its own variable name, of the format say, tsn, where n is the n'th simulation. i.e. ts1, ts2, ts3, ts4, .... , ts1000 The way I am thinking of doing this is placing the following code within another loop, and the 'tsn' assignment should

Hi, this is my first time using R. I want to simulate the following process: "in a population of size N, there are i individuals bearing genotype A, the number of those bearing A is j in the next generation, which following a binominal distribution (choose j from 2*N, the p is i/2*N), to plot the probability of the next generations, my script is as follows. It cannot run successfully,

Hi: I was wondering whether I can find some help for computer simulation of stochastic processes (e.g. Brownian motion), for pedagogicl/instructional purposes. Any help would be appreciated. thanks, Ravi.

Hi, I apologize in advance if this is the wrong area to post this message. I would like to know if there is an "R" equivalent for the "S+finMetrics" package? I'd like to be able to use "R" to go through the examples provided in the book "Modeling Financial Time-Series with S-Plus" (E. Zivot and J. Wang). I was told that "R" and

Dear all of you, I would like to get some help from you. Here I attach you a model, that I would like to be stochastic so I would need each time the value of x changed, the values of parameters (ss, emrg, gf, spp, sr) did too according to a normal distribution, with mean its value and standard deviation given by the parameter name preceded by SDV (In the case of parameter ss, mean is 0.3 and