Standard errors for estimates of smoothing parameters

My question is based on the "forecast" package for R used in Forecasting with Exponential Smoothing. The State Space Approach - Hyndman et al. 2008. I am using the ets function to estimate the parameters of a model.

Is there a way to obtain standard errors for the estimates of the smoothing parameters?

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Not all methods lead to analytic expressions (preferably based on proper asymptotic results) that provides this.

But the bootstrap allows you to approximate this via simulation. In essence, you generate (lots of) surrogate 'fake' data sets, employ your estimator on each of these and then use the population of your estimates to make inferences. However, doing bootstrapping in a time series context has its own challenges...

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+1 it is always nice to bootstrap something. On the other hand every challenge is easy with R: stat.ethz.ch/R-manual/R-devel/library/boot/html/tsboot.html –  mbq Aug 31 '10 at 19:47
Thanks Dirk and mbq. I have one time series I collected. I am not using a data generating process to produce different series. So in this case I understand that I cannot get std deviations? Please correct me if I'm wrong. Thanks again! –  noworries Aug 31 '10 at 23:51
You could try 'chunked' resampling. Google for block bootstrap. –  Dirk Eddelbuettel Sep 1 '10 at 1:14