# Questions about the paper “Inferring causal impact using Bayesian structural time series models”

I am simulating this model in the paper Brodersen et al. (2015) and found I have difficulties on figuring out how to choose the starting point of Bayesian inference.

To be explicit, to my understanding, if I choose Durbin and Koopman (2002)'s simulation smoother (I think this is what the paper used) to simulate the latent state $\boldsymbol \alpha$ from $p(\boldsymbol \alpha| \boldsymbol y, \theta, \beta, \sigma^2_\epsilon)$, I need to choose the starting points of $\eta_{\mu,t}$, $\eta_{\delta,t}$ and $\eta_{\gamma,t}$ in order to generate a set of new dataset $\boldsymbol w^+$, $\boldsymbol \alpha^+$ $\boldsymbol y^+$. However, I cannot figure out how to choose these values. If I generate a value or use the mean from the prior $1/\sigma^2 \sim Gamma(10^{-2}, 10^{-2} s_y^2)$ suggests in the paper to inference $\theta \sim P(\theta| \boldsymbol y, \boldsymbol \alpha, beta, \sigma^2_\epsilon$, the starting points would be too big.

Another question is how to choose the starting points for $\boldsymbol \beta$ and $\sigma^2$?

Last, I found the main part of R package CausalImpact is being compiled and thus I cannot open the code to see how it works. If anyone has the code or similar, would you help to share it if possible?

Thanks to everyones help.

• CausalImpact code is here github.com/google/CausalImpact – jaradniemi Jun 5 '15 at 16:53
• Thanks to your reply. It is a good resource. If check this code carefully, the core part of this code is being compiled into .so file. So I dont know how the bayesian inference works exactly. This is the part I am asking for answer. – Po Ning Jun 5 '15 at 17:37