# How do I obtain the standard errors for Ornstein Uhlenbeck parameter estimates?

I have used least squares estimation to obtain estimates for parameters to be used in Ornstein Uhlenbeck process. Now, I would like to compute the standard errors of estimates.

$$đđđĄ=đ(đâđđĄ)đđĄ+đđđđĄ$$ (Ornstein Uhlenbeck process and parameters to be estimated)

The regression is as follows:

$$S(t+1)=a*S(t)+b+đ$$

The estimates for parameters are obtained from the regression coefficients as follows:

$$đ=â(lnđ)/ÎđĄ, đ=đ/(1âđ)$$

and

$$đ=sd(đđĄ) * \sqrt{2đ/(1âđ^{â2đÎđĄ})}$$

I can obtain the standard errors for a and b, but how would I be able to obtain standard errors for the parameters?

Any help is greatly appreciated, I apologize the messy layout of formulas.

If you are reporting to physicists or chemicists, I would recommend the Gaussian error propagation law, because that is what they usually apply in such cases: $$(\Delta f)^2 = \left(\frac{\partial f}{\partial a} \Delta a\right)^2 + \left(\frac{\partial f}{\partial b} \Delta b\right)^2 + \ldots$$ If you are reporting to statisticians, a resampling method like the bootstrap or the jacknife might raise fewer eyebrows ;-) The jackknife is easier and yields reproducibale results, whilst the bootstrap yields a random (albeit not much different) result.