Can I compare the same Linear Regression Model over time? I'm performing a linear regression model with respect to voter participation for one of my class papers and I was curious if I could apply the same model to various time periods and then analyze/compare and contrast the differences in the significance of the model overall and the individual variables.
For instance, let's say my voter participation model for 2020 is based off of 2019 poverty, 2019 unemployment data, 2019 home ownership rates, etc.
Could I apply the same model to 2018, 2016, 2014 based off of their respective years? I know that a time series analysis would be the ideal situation here, but I am not familiar with those techniques and additionally, some of my data are 5-year estimates and I am unsure if using those in a time series analysis is a good idea.
The comparison would look like this basically (this is a hypothetical): In the 2020 regression model the percentage of the government GPD was insignificant whilst in the 2018 model, it was significant at a 0.05 level. This could perhaps be because the size of the local government in determining voter turnout through a rational choice voter model is overshadowed by the significance of a presidential election.
Any assistance is appreciated. Thanks! :)

Well, for the hypothetical example text the answer is "definitely no", the difference between significant and insignificant is not really interpretable
(full story here: http://www.stat.columbia.edu/~gelman/research/published/signif4.pdf)
On the other hand, there's nothing wrong with the question, sounds interesting! Probably one reasonable answer is to build a multilevel model? Model the whole data set all together, in such a way that the model is aware of the grouping by year. Something like this tadpole example: https://bookdown.org/content/3890/multilevel-models.html
There are probably other reasonable ways too! But comparing significant / not significant in fits to multiple subgroups is not one unfortunately
