Linear regression on Olympic data - How consistent are the effects over time? I have performed a linear regression to predict Olympic medal count from Population and GDP for year 2008, 2012 and 2016

I have been asked to explain how consistent the effects of Population and GDP are, over time. I understand that I am meant to look at the coefficients estimate for GDP and Population. Am I simply meant to compare the values of GDP for different years and the same for Population ? 
 A: Rather than fitting three seperate models, you would be better off consolidating your data and then running a single model that also uses Year as one of the predictive variables for Medal.  Having said this, remember that you (presently) only have three years of data, so that is not really enough to include interaction effects between the year and the other variables.  Consequently, it will be dubious to make findings about how the effects of population and GDP change over the years.  If you are serious about conducting analysis of this kind, I recommend you get Olympic data going much further back in time so that you can include a large number of years in your analysis.  If you can get sufficient data it may then become reasonable to look at the interaction of year with the other predictors.  You should also consider using some kind of count-based regression model for the  medal counts.  I believe there is quite a bit of existing statistical literature modelling Olympic medal counts, so it would be worth looking at this to get a starting point for your analysis.
A: For anyone else dealing with a similar problem, I think this is how it is meant to be done in R. 

