20 Jul
2020
20 Jul
'20
9:44 a.m.
Hi Nick, the way to think about it is that if you omit a variable, you're
implicitly including the variable and restricting its coefficient (in any
regression) to 0, unless that relationship is picked up by other variables
you do include.
Gary
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On Mon, Jul 13, 2020 at 7:28 PM Nick Eubank <nick(a)nickeubank.com> wrote:
Hi All,
I'm working with panel data (state-years) and a difference-in-difference
design. I'm looking to fill some values in the dependent variable time
series.
My main specification is thus just two-way fixed effects (state FEs and
year FEs) and a treatment variable.
I've gotten the best results modeling the missing data by running a local
polynomial regression (a la Honaker and King 2010) for each state, and then
using those predicted values as the predictor in Amelia. (i.e. run I run a
local polynomial of my DV against time for state S, fill those values in to
my predictor variable for state S, repeat for all states)
Everything I've read says I should definitely include all variables I plan
to use in my analysis in Amelia, but I worry about failing to meet
multi-variate normality conditions with the FEs, and running the model with
and without them, I'm not sure they're adding much.
Do I *need* to include all those FEs (i.e. will I introduce some weird
bias in my subsequent analysis if I don't)? And if I do, is there anything
I can do to deal with them definitely not being multi-variate normal (or do
I not need to worry about that)?
Thanks!
Nick