Frisch–Waugh–Lovell theorem

In econometrics, the Frisch–Waugh–Lovell (FWL) theorem is named after the econometricians Ragnar Frisch, Frederick V. Waugh, and Michael C. Lovell.[1][2][3]

The Frisch–Waugh–Lovell theorem states that if the regression we are concerned with is:

${\displaystyle Y=X_{1}\beta _{1}+X_{2}\beta _{2}+u}$

where ${\displaystyle X_{1}}$ and ${\displaystyle X_{2}}$ are ${\displaystyle n\times k_{1}}$ and ${\displaystyle n\times k_{2}}$ matrices respectively and where ${\displaystyle \beta _{1}}$ and ${\displaystyle \beta _{2}}$ are conformable, then the estimate of ${\displaystyle \beta _{2}}$ will be the same as the estimate of it from a modified regression of the form:

${\displaystyle M_{X_{1}}Y=M_{X_{1}}X_{2}\beta _{2}+M_{X_{1}}u,}$

where ${\displaystyle M_{X_{1}}}$ projects onto the orthogonal complement of the image of the projection matrix ${\displaystyle X_{1}(X_{1}^{\mathsf {T}}X_{1})^{-1}X_{1}^{\mathsf {T}}}$. Equivalently, M_X1 projects onto the orthogonal complement of the column space of X₁. Specifically,

${\displaystyle M_{X_{1}}=I-X_{1}(X_{1}^{\mathsf {T}}X_{1})^{-1}X_{1}^{\mathsf {T}},}$

and this particular orthogonal projection matrix is known as the annihilator matrix.[4][5]

The vector ${\textstyle M_{X_{1}}Y}$ is the vector of residuals from regression of ${\textstyle Y}$ on the columns of ${\textstyle X_{1}}$.

The theorem implies that the secondary regression used for obtaining ${\displaystyle M_{X_{1}}}$ is unnecessary: using projection matrices to make the explanatory variables orthogonal to each other will lead to the same results as running the regression with all non-orthogonal explanators included.