This question is regarding “Estimating cross-section common stochastic trends in nonstationary panel data”, Bai (2004). On p. 152 he writes: The model: $$X_{it}=\lambda_{i0}F_{t}+\lambda_{i2}F_{t-2}+e_{it},\,Eq.\,\left(1\right) $$can be rewritten as: $$X_{it}=\left(\lambda_{i0}+\lambda_{i2}\right)F_{t}-\lambda_{i2}\left(\Delta F_{t-1}+\Delta F_{t-2}\right)+e_{it},\,Eq.\,\left(2\right) $$where $X_{it} $ and $F_{t}$ are scalars (for simplification) and $F_{t}$ is a random walk, $F_{t}=F_{t-1}+u_{t}$.
If I multiply $Eq.\,\left(2\right)$ out I get:
$$X_{it}=\lambda_{i0}F_{t}+\lambda_{i2}F_{t-2}+\lambda_{i2}\left(F_{t}-F_{t-1}-F_{t-2}+F_{t-3}\right)+e_{it}$$
So unless the term $\lambda_{i2}\left(F_{t}-F_{t-1}-F_{t-2}+F_{t-3}\right)=0 $ the statement above will not hold. My problem is that I cannot get this term to equal zero, even after using the definition for $F_{t}=F_{t-1}+u_{t}$
Before this he considers a more general model but when I can't rewrite this simpler one there is no point in looking at the more general one. Any hints on how to solve this would be appreciated.