My time series model suffers from multicollinearity between two independent variables.
When taking one of the variables out of the model I obtain a coefficient of 0.08 for variable 1 but when including both variables simultaneously the sign reverses and I get a coefficient of -0.33 for variable 1 and 0.45 for variable 2.
My professor said that if both variables were perfectly correlated the sum of both coefficients would be 0.12 and that 0.08 was therefore a sign for multicollinearity. Does anyone know a paper confirming what she said?
Also when running the regression exluding variable 1, I was expecting the coefficient for variable 2 to be somewhat similar to the coefficient of variable 1 (using the same logic as above, i.e. adding up the coefficients should yield a coefficient that is similar to running the regression alone) but the coefficient is very different (0.3). Can somepne explain why this might be the case?