search for: epsilon_t

This is a time series\optimization rather than an R question : Suppose I have an ARMA(1,1) with restrictions such that the coefficient on the lagged epsilon_term is related to the coefficient on The lagged z term as below. z_t =[A + beta]*z_t-1 + epsilon_t - A*epsilon_t-1 So, if I don't have a facility for optimizing with this restriction, is it legal to set A to something and then Optimize just for the beta given the A ? Would this give me the sam...

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta y_{t-1}^2 I looked through documentation of garchFit() from the fGarch library but didn't find a way to include exogeneous variables like x_t....

Hi, I want to fit a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

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 spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL, extern...

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

hello, I have been using the per function in package longmemo to obtain a simple raw periodogram. I am considering to switch to the function spec.pgram since I want to be able to do tapering. To compare both I used spec.pgram with the options as suggested in the documentation of per {longmemo} to make them correspond. Now I have found on a variety of examples that there is a shift between

...Regards Liviu On Mon, Aug 23, 2010 at 5:59 AM, Aditya Damani wrote: > Hi, > > I want to fit a mean and variance model jointly. > > For example I might want to fit an AR(2)-GARCH(1,1) model i.e. > > r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t > > where a_t = sigma_t*epsilon_t > > where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 > > i.e. R estimates a constant_term1, b, c, constant_term2, p, q > > TIA > Aditya > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailm...