I have some data that varies with time and some that stays constant (e.g., location, race stay constant).  Is it possible to implement a **mixed** time-varying coefficient model in python?  What I mean is:

$$
y = \beta_0(t) \cdot x_0 + \beta_1(t) \cdot x_1 + \beta_2 \cdot x_3 + \beta_3
$$

where $\beta_0, \beta_1$ are time-varying (dependent on $t$), and the rest of the betas are not.

This is similar to the SAS package TVEM (page 7, eq. 4 of [this doc](https://methodology.psu.edu/sites/default/files/software/tvem/TVEM_3.1.0.pdf)).  

It seems this might be possible in R using the `gam` models, but I'm not very familiar with R.  Any clues to approach this in python (or get me started) would be helpful.  I'm familiar with the `statsmodels` package and the `scipy` stack.