I'm trying to model how one time series A (transactions in an asset in some market) leads another time series B (transactions in a related asset). In this sense, I guess I'm trying to forecast B using lagged values of B and lagged values of A.
What tool seems to fit the description of what I'm trying to do? I was thinking ARDL models fit this kind of problem quite well, but I haven't found a ready-to-go implementation in Python.
There's also ARIMAX but from the descriptions it seems that this would be trying to predict B using lagged values of B and contemporary values of A. Is it possible to predict B using only non-contemporary values of A and B (is it as simple as just shifting A?) Lastly, it's monthly data, but going in, I'm unaware of the lag factor to be used.
Although I've read a bit around this site, very new to time series and appreciate all advice, cheers.