st: multilevlel panel data

[Hobst <tobias.friedli@access.uzh.ch>]

Hello Statalist

I have a panel dataset (12 Quarters, 300 car-models, 38 car-brands). The car
models are obviously nested within brands. The dependent variable is a
loyalty measure (y), and the main independent variable is the a dummy
variable (x) for introduction of a new-generation car model (controlled for
ad-spendings and other marketing mix measures).

As the loyalty measure is expected to be dependent on omitted variables and
the mean loyalties are assumed to be model specific, i estimated the
regression using the fixed effects model xtreg, fe. (hausman test confirms
the use of fe over re)

More precisely: xtreg y x L(1/3).x F(1/3)x ads L.ads, fe vce(cluster model)

(1) First of all do you think this model (especially the use of the lags to
model the development of y over time around the new introduction date) makes
sense? Or is there maybe a better approach?

As the models are nested in brands i would like to also control for brand
specific effects and error term correlation on brand level. I read a lot
about the use of panel data and hierarchical models and think the use of
-xtmixed- would be useful in this case. However i dont get how i can
incorporate time-series and multilevel in the regression:

would something like that be the right way:

xtmixed y x L(1/3).x F(1/3).x ads L.ads || brand: || model:, mle

(2) or am i completely wrong here? Is the datevar (specified under "xtset
model datevar") now still taken into account as the lowest level of the
hierarchy? I dont really get this one.

Any help on this problem would be much appreciated. Thank you in advance for
your support.

Regrads,
Toby


Tobias Friedli
Bachelor Student at the University of Zürich,
Chair for Market Research


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