Hi Folks!

While running a robust random effects regression I've noticed that adding an additional variable to my regression has resulted in a smaller R

I don't think the problem has anything to do with multicolinearity, as when I examined the correlation matrix both exports and production correlation coefficient was 0.61.

While running a robust random effects regression I've noticed that adding an additional variable to my regression has resulted in a smaller R

^{2.}My first regression examined the effects the investment share, oil exports and political rights(scale variable) have on GDP. All variables bad PR are in log format. Anyway I decided to add additional variables in the model , oil production. Anyway adding this additional variable has resulted in my r2 reducing. Below shows the output.Code:

xtreg log_rgdpl log_ki log_exports PR, re robust Random-effects GLS regression Number of obs = 1625 Group variable: id Number of groups = 65 R-sq: within = 0.4017 Obs per group: min = 25 between = 0.5872 avg = 25.0 overall = 0.5073 max = 25 Random effects u_i ~ Gaussian Wald chi2(3) = 83.58 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 (Std. Err. adjusted for 65 clusters in id) ------------------------------------------------------------------------------ | Robust log_rgdpl | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- log_ki | .0715439 .04157 1.72 0.085 -.0099317 .1530195 log_exports | .1007198 .0113213 8.90 0.000 .0785305 .1229091 PR | -.0086778 .0119547 -0.73 0.468 -.0321086 .0147529 _cons | 8.546594 .1662514 51.41 0.000 8.220747 8.87244 -------------+---------------------------------------------------------------- sigma_u | .71276532 sigma_e | .15705413 rho | .9536963 (fraction of variance due to u_i) ------------------------------------------------------------------------------

Code:

. xtreg log_rgdpl log_ki log_exports log_oilp PR, re robust Random-effects GLS regression Number of obs = 1625 Group variable: id Number of groups = 65 R-sq: within = 0.4455 Obs per group: min = 25 between = 0.5414 avg = 25.0 overall = 0.4848 max = 25 Random effects u_i ~ Gaussian Wald chi2(4) = 122.88 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 (Std. Err. adjusted for 65 clusters in id) ------------------------------------------------------------------------------ | Robust log_rgdpl | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- log_ki | .0284645 .0443018 0.64 0.521 -.0583655 .1152945 log_exports | .0728598 .0129074 5.64 0.000 .0475618 .0981578 log_oilp | .0509208 .014486 3.52 0.000 .0225288 .0793129 PR | -.0117452 .0113979 -1.03 0.303 -.0340847 .0105944 _cons | 8.544471 .1809206 47.23 0.000 8.189873 8.899069 -------------+---------------------------------------------------------------- sigma_u | .66937021 sigma_e | .15124515 rho | .95142586 (fraction of variance due to u_i) ------------------------------------------------------------------------------

## Comment