Time series Data : Regress absolute values or regress the %growth of the values? I am doing a time-series data analysis. The idea is to produce a forecast from the regression output. 
I am regressing Air traffic passengers of country A with GDP/capita of country A.
I am getting very high R² (0,97) by regressing absolute values of Air traffic passengers of country A with GDP/capita of country A (p.value<0,05)
I am getting low R²(0,35) by regressing the same variables but that time, by taking the %growth of those variables. (p.value<0,05)
What is the most accurate in this case? regress absolute values or regress the %growth? 
 A: Use the observed values as there may be lag structure needed for GDP (X) that may be different than the lag structure need for air traffic passenger data (Y) . You can always subsequently express the change in passenger forecast as a percentage of the previous value as it relates to the change in GDP as it relates to the previous value of GDP .
Finally to get correct/honest prediction limits for passenger data (Y) you may/will need to account for the uncertainty in GDP (X) predictions and possible re-occurring anomalies.
You might want to read https://autobox.com/pdfs/regvsbox-old.pdf which discusses some of the opportunities that are available when you have time series data and some of the pitfalls when you use simple OLS.
I surmise you ran a simple OLS of Y versus X and obtained .97 as the Rsq AND then
you created two new series Y1 and X1 where Y1=[Y(t)-Y(t-1)]/Y(t-1)  and X1=[X(t)-X(t-1)]/X(t-1) and obtained .35 when you regressed Y1 and X1 . This specification is probably suspect for a ton of reasons whereas a SARMAX https://autobox.com/pdfs/SARMAX.pdf is much more general approach. Your specification of the model (two ratios)  is a possible model but not probable because it is based upon presumption. 
