Hi All,
I am trying to code an R script which gives me the time varying parameters of
the NIG and GH distributions. Further, becasue I think these these time varying
parameters should be more responsive to more recent observations, I would like
to include a weighted likelihood estimation proceedure where the observations
have an exponentially decaying weighting rather than the equal weighting
implied by a standard MLE approach.
The optimization function which leads to my parameter estimates is given by;
gh.fit = try(optim(vega, negllh,hessian = se,pdf = gh.pdf,
tmp.data = data, transf = transform, const.pars = vars[!opt.pars],
silent = silent, par.names = names(vars), ...))
param.est = gh.fit$par
In the above, vega are the parameters to estimate and pdf is the GH pdf. This
seems to work well for the case where observations are equally weighted.
However, I'm stuck on how to include a weighted vector (w_i) to turn this
problem into a weighted ML optimization.
Would you please be able to suggest a function or change in code which may
allow me to do this?
Thank you in advance for your time.
Jason
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