In some cases it may make sense to create a model with a lower resolution, even if you have access to data with higher resolution.
One example is if the time series consist of the differences of other time series which do not change very often, such as the trading prices of illiquid stocks. If you use such time series with too high resolution, you risk that most of the values are zero, making it difficult to detect correlations between the time series. In this case the resolution should be reduced to make it harmonise with the rate at which the stock is actually traded.
Another case is where one time series influences another, but where there is a delay in the influence. If you, for example, have data with daily resolution, but it takes a few days before a change in one time series propagates to the other time series, the model might fail to pick up the correlation, and it may be better captured if you reduce the resolution to weekly.