A simple way to deal with multicollinearity

Despite the long and frustrating history of struggling with the wrong signs or other types of implausible estimates under multicollinearity, it turns out that the problem can be solved in a surprisingly easy way. This paper presents a simple approach that ensures both statistically sound and theoretically consistent estimates under multicollinearity. The approach is simple in the sense that it requires nothing but basic statistical methods plus a piece of <italic>a priori</italic> knowledge. In addition, the approach is robust even to the extreme case when the <italic>a priori</italic> knowledge is wrong. A simulation test shows astonishingly superior performance of the method in repeated samples comparing to the OLS, the Ridge Regression and the Dropping-Variable approach.