[Limdep Nlogit List] can i pick your brains?

Durham, Cathy cathy.durham at oregonstate.edu
Tue Mar 24 06:32:09 EST 2009

William you might want to try the TSCS panel estimator and use the
options that allow for autocorrelation over time and group=wise
heteroscedasticty etc. It's especially useful with more time periods
than cross-sections, but I think t=20 is enough to gain from the
autocorrelation options. Given your limited cross-section variation over
time in some variables you should be very thoughtful about your use of
cross-section dummy variables; they will collect much of the information
in those cross-section variables that don't change much. At least try it
with and without those dummies, before you accept findings of
insignificance for those cross-section variables. This is even more
critical if you are testing lagged dependent variables which can easily
be biased in this framework. If you are let me know, I have some
references I can recommend. Cathy

From: limdep-bounces at limdep.itls.usyd.edu.au
[mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of Bill Spitz
Sent: Monday, March 23, 2009 9:57 AM
To: listserv LIMDEP
Subject: [Limdep Nlogit List] can i pick your brains?

This may not be the best forum for this, but I'm trying to figure out a
reasonable modeling approach for the following:

I'm trying to explain changes in levels of airline service (measured as
flights per day) across many different airports over about 20 years. So
I have a panel set of observations y(it) for airport i and time t.

Here's the rub. Most of the candidates for explanatory variables are
-- "i-dominant" variables, ie., they vary significantly from one airport
to another, but hardly at all over time, eg. local real income,
population, distance to nearest hub, etc, (these serve to essentially
provide the scale of operations at the airport); OR
--"t-dominant", they vary significantly over time, but not by airport,
eg. fuel prices, airline costs, etc.

So I don't really have the traditional x(it)-type explanatory variables
that vary in both dimensions where one would employ a standard panel
estimator. I have quickly gotten lost in the LIMDEP manuals with all the
different possible panel models that are available.

Any insights into how best to approach this from a modeling standpoint
would be appreciated.

William Spitz

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