Our variables are insignificant. What are the reasons? But Adjusted R square is good Percentage. [![enter image description here]]
[![enter image description here]]mgur.com/CQtdk.png
If the model fits well (good
R^2), but the variables cannot be estimated reliably (not significant), here's a couple of things that may be the case
In both cases, it can help to remove explanatory variables, in particular the ones that covary with other variables.
First, as others point out, one variable is highly significant. Also, one is close.
Second, you may have collinearity. VIF is not the best measure of this. It's better to use condition indexes, as I showed in my dissertation and as David Belsley showed in much more detail in Conditioning Diagnostics: Collinearity and Weak Data in Regression. If you do have collinearity, there are a number of solutions, perhaps the most relevant here is ridge regression.
Third, although it's a relatively minor point, I would transform "remittances" and "gold" by dividing them by 1,000,000. This will make the output easier to understand.