Interpreting regression coefficients and analysis

I am using a multiple linear regression model to forecast exchange rate changes with the interest rate differential (IRD) being one of my coefficients.

Per my regression results, I am obtaining a positive coefficient for the interest rate differential, suggesting that an increase in the interest rate differential will be conducive in an increase in the exchange rate.

Say for example that my interest rate differential is calculated as US interest rate - EU interest rate.

When I say that a one unit increase in the interest rate differential, ceteris paribus, there will be a corresponding (IRD coefficient)*1 increase in the dependent variable (exchange rate), is it wrong to assume that US interest rates > EU interest rates?

Does a one unit input increase in the IRD suggest that the (US - EU) differential is becoming bigger by 1 unit in turn allowing me to conclude that US interest > EU interest?

I have also conducted a hypothesis test on the mean US - EU differential which allowed me to conclude that the mean was greater than 0.

What further steps will i have to take to conclude that a one unit increase in the IRD suggests that US interest rates are higher than EU?

Alternatively, is there any way to ascertain that the IRD is positive in my model, in turn allowing me to conclude that US rates are higher than EU?

Thank you.


Less is more. Stay pure. Stay poor.
I didn't read all of your post, but just wanted to say that if you are using multiple LR to forecast make sure you are using prediction intervals with your predictions, and that traditional LR does not control for dependence in data, meaning if the series isn't stationary intervals may not funnel out when predicting into the future.