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