Several questions about the codes

Hi CausalImpact team,

I'm trying to use the CausalImpact package to implement one of our company's project. Here are some questions after I reviewing the raw code from this repository:

1. I noticed that in the file [impact_model.R](https://github.com/google/CausalImpact/blob/27db39108e91ab4d4a3a665b18814df7c2f49a44/R/impact_model.R) The `expected.model.size` of the function `bsts` is fixed to be 3 (by the variable `kStaticRegressionExpectedModelSize`). Is there any reason for this parameter to be fixed as **3** ? Also, Is that possible to make it adjustable in the future is by `model.args` ?

2. I also noticed that both pre.period and post.period control groups' data are included in one `bsts` function, which may cause while estimating the posterior distribution of the coefficients by using the `pre.period` data, the `post.period` information is also used.

For example: If my dataset `test` is like:

date_period, target, control_1, control_2
1, 100 , 3, 0,
2, 90, 2, 0,
3, 80, 2, 0,
4, 70, 3, 1,
5, 60, 3, 1

code of generating this test data:

```
target = c(100, 90, 80, 70, 60)
control1 = c(3,2,2,3,3)
control2 = c(0,0,0,1,1)

test = data.frame(target, control1, control2)
```

and
`pre.period = c(1,2), post.period = c(3,4)`

In this case, if you run the command:

```
impact <- CausalImpact(test, pre.period, post.period, model.args = list(standardize.data=F, niter = 100),alpha = 0.9)
coef = as.data.table(impact$model$bsts.model$coefficients)
```

you can find control_2 also shows non-zero coefficients in each iteration.

This is of course an extreme case. My question is, have you tried how different will be the results: between using current method, and the traditional `predict(bsts)` after building the model by only using pre.period data?

Thank you in advance!