May I have a question on how to solve the following problem by R code?
Mainly we want to solve the equation show in the attached image. The equation is a continuous version of Markov process.
In the equation, we have been able to achieve two things using R code:
 From year-2009 sample data, we can estimate the marginal density ‘f(x ; 2009)’ by using R function ‘density()’
 From appropriately grouped all sample data, we can estimate the conditional density ‘g(z|x ; 1)’ by using R function ‘cde()’ from hdrcde package
Now we are searching and reading reference docs on how to solve for the long-run (ergodic) distribution ‘f(x)’. Much appreciated if any suggestions on solution steps using R; and we will be happy to provide additional references. Thanks a lot!
I haven't tried this, but I am pretty confident that using dlmFilter()
with fictitious future values of the observations set to NA should do
Hope this helps,
On Sat, 2011-10-08 at 13:21 +0000, YuHong wrote:
> May I have a question about dlmForecast() function in the package 'dlm'?
> This function 'dlmForecast()' currently only deals with constant
> models. May anyone suggest on how to predict using non-constant
> model? Thanks a lot!
> Hong Yu
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