I'm sorry I make reference to Stata here, but it's just to illustrate the point, the question is about whether it is correct or not using lagged variables in interaction terms.
I have a linear model that I am estimating using FE and RE, in which my explanatory (continuous) variable is lagged, something like this:
$$ y = \alpha + \beta x_{t-1} + \epsilon $$
Stata: xtregar y L.variable, fe/re
For my analysis, I need to include a categorical interaction term. Stata, by using the graphical interface, does not allow me to use lags with my continuous variable, but it does give me results if I add 'L' manually, and these are very different to those without the L.
What Stata's graphical interface allows me to do is:
xtregar y L.variable dummy#c.variable i.dummy, fe
And what I think I need is:
xtregar y L.variable dummy#cL.variable i.dummy, fe
I am confused since I imagine that if adding lags was OK, Stata would allow me to do so, wouldn't it?
- Is it correct to add lags to an interaction term?
- In case of that the answer to (1) is no: If I remove the
variable (
L.variable
), is it ok to add lags to the interaction term?
A related question:
What to do if the variable is not significant but the interaction term is?