Dear Clive,
if you have not thought about that, it may be useful: you could use the
UEFA coefficient in your regression, they've calculated this measure at
least since 1960 (http://www.xs4all.nl/~kassiesa/bert/uefa/index.html) -
you could argue that it is a proxy for unobserved country effects
(strength of the national league).
Hope this helps,
Johannes
Clive Nicholas schrieb:
> Sorry folks, but I need to add two errata and one further
> consideration to my earlier post, if only to help out other
> Statalisters who may wish to contribute:
>
> (E1) Either I have collected some data for this or I have not: the
> answer, as you all probably guessed, is that I have;
>
> (E2) The last refererence should, in fact, read: Stimson JA (1994)
> "Regression in Space and Time: A Statistical Essay", American Journal
> of Political Science 29 (4): 914-47, and _not_ 1971 as I printed!
>
> (C1) I have perhaps have not thought as hard about the use of lagged
> variables as I should have done for these (at the moment, theoretical)
> models. I should give here a very brief explanation as to what these
> WFERs actually are. Essentially, these are based on international
> chess ratings devised by the Hungarian-American physics lecturer (and
> chess master), Professor Arpad Elo (1903-92). Incredibly, some bloke
> with bags of SQL skills (and lots of time), has tweaked Elo's original
> formula and calculated ratings for every single national football team
> in the world from their very first game onwards (for more, see:
> http://www.eloratings.net/world.html). Given the way it is calculated,
> a team's 'past form' is already built into their current WFER, so I am
> unsure as to whether lagged variables would be useful for my models
> here. I guess this is another question for the list. If I were an
> economist, I would be in three minds about it!
>
> Ta very much, once again.
>
>
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