What is the most important factor in sample selection for time series models?

Is there some convention in sample selection for time series analysis? And what is the most important factor in this regard?

I mean, I have seen Kónya, I. (2018) use annual data from 1995–2016 to estimate a DSGE model.

I have seen Uribe, M., & Schmitt-Grohé, S. (2017) use annual data from 1980-2011 to estimate a VAR model.

I have seen Drechsel, T., & Tenreyro, S. (2018) use annual data from 1900-2015 to estimate a VAR and DSGE model.

Shousha (2016) uses a quarterly sample from 1994–2013.

What appears to be the pattern in these papers for me is the following;

1. First, choose a sample period you want to analyze, maybe, based on a structural break in the data or maybe before or after some event.
2. Then choose which frequency you want to analyze…annually, monthly, quarterly.
3. Then use all available data you can get based on 1 and 2.
4. Then check the parameters of the model if they are stable after estimation.

With all that said, there is a ton of information on the internet saying, for example, that sample size should be at least 50 or 80 or some threshold. Meanwhile, I do not see these thresholds in the literature. Instead, what I see is that researchers focus on some period they want to analyze at some desired frequency, not caring so much about the number of observations (which as I have shown above can be even 20).

By the way, let’s say the parameters of the model are stationary for the sample 1980-2018. Then quantitative results would change if say the sample 1985-2018 is used, although both models are valid. I guess one should place more weight on qualitative results (like signs) than on quantitative results which are relatively more sensitive to sample size.