Two of your explanatory variables, SHR and DR, show too high standard errors yet you reject the null hypothesis. This may be a sign of multicollinearity. Are these variables defined through each other?

I suggest you compute the correlation matrix of your variables and check if these variables show high correlation. This is okay if your intention is to do prediction (see Gujarati's Basic Econometrics) but in this state you cannot proceed with statistical inference as two of your estimations are highly imprecise.

You can drop variables based on the correlation matrix, or based on your theorization. I would proceed with the latter before the former.

Two of your explanatory variables, SHR and DR, show too high standard errors (with respect to their estimated coefficients) yet you reject the null hypothesis. This may be a sign of multicollinearity. Are these variables defined through each other, e.g. is one of them a linear combination of the other?

I suggest you compute the correlation matrix of your variables and check if these variables show high correlation. This is okay if your intention is to do prediction (see Gujarati's Basic Econometrics) but in this state you cannot proceed with statistical inference as two of your estimations are highly imprecise.

You can drop variables based on the correlation matrix, or based on your theorization. I would proceed with the latter before the former, as if your model shows signs of multicollinearity, this can be a consequence of sampling (https://online.stat.psu.edu/stat462/node/177/).