Dear Statalist users,
I am kind of new with Stata and I'd really appreciate your help.
I have been working on trying to find a correlation between the Fed's Quantitative Easing program (my main independent variable) and aggregate consumption (my dependent variable). In particular I regress the total (logged) monthly consumption on the total (logged) monthly number of assets owned by the Fed (as you know, QE consists in asset purchases) plus a number of control variables. Therefore, my data consists in a time series with a monthly frequency from 2003 to 2019. Please note that all of the data are seasonally adjusted and/or inflation adjusted, in order to get more precise estimates.
Unfortunately, I cannot paste here the code for my regressions because of character limits.
Anyway, my simple OLS regression has an R-sq of 0.99 and most of the regressors are statistically significant. I conducted a Durbin-Watson test and the result was .75, meaning that there is some autocorrelation.

Therefore I tried to perform a first difference regression. The R-sq. is much smaller (0.16) and, more worryingly, most of the regressors are now insignificant and the coefficients very small (in the 0.00 order), making it hard to draw conclusions from the results.

My questions for you are the following: is it true in this case that the regression with the lowest R-sq. is preferrable to the one with a very high R-sq. because of autocorrelation? Would it be a good idea to use the simple OLS regression with Newey-West standard errors, considering that autocorrelation does not lead to biased estimates but to incorrect standard errors?

I hope I have been clear enough.

Thank you very much in advance