# Can VAR models handle time series with different lengths?

I'm trying to better understand how Vector Auto-Regression models work in practice. It seems to me that a real world time series data set is likely to have time series of different lengths, but I can't figure out how a VAR model would handle this situation.

1. If we shorten the time series in order to bring them all to the same length, we loose a lot of valuable information.
2. Zero padding the shorter time series to be the same length as the longest time series would mess with the quality of the coefficients that model the time series.
3. Imputation might work for single missing values here and there, but I don't see how it could work when an entire leading chunk is missing from the time series.

Is there a way to use VAR on groups of time series with different lengths?

• I'm no VAR expert but I've never heard of shortening everything to be the same length. It kind of makes sense that I haven't because , in a VAR, every variable in the VAR has the possibility of causing or being caused by of any other variable in the VAR so, if you have a longer length for one of the variables $Y$ compared to say $X$, then $X$ can't cause $Y$ or be caused by $Y$ because it doesn't occur at the same time as $Y$. You should include variables in the VAR that occur at the same time and for as long as all of the others. – mlofton Mar 8 at 4:05