This question is not gretl-specific, but answers might show up gretl
features and hopefully it may be of interest.
My dependent variable is the quantity demanded of a certain good, an
hourly time series extending over two years. It exhibits strong
seasonality, both by hour of the day and season of the year; it has
an upward trend; and it's clearly affected by various measures of
weather (temperature, humidity, wind speed). Plain OLS produces
quite a decent fit, but by inspecting the loglikelihood-for-level
figure produced by gretl (via the Jacobian) I can see that taking
the log of the dependent variable gives a better fit. All fine.
However, total demand is the sum of demand from two classes of
consumer -- call them A and B -- and I'm wondering if a better fit
can be obtained by summing the fitted values from separate
regressions, with dependent variables the demand from consumers A
and B respectively. (Note: a Chow test in dummy variable mode is not
applicable, the unit of observation is the hour, not the
transaction.)
My thought was: compute two SSRs in levels, using (restricted) the
exponentiated fitted values from the overall model and
(unrestricted) the sum of the exponentiated fitted values from the
two consumer-class models, then calculate an F test based on the
difference of SSRs in the usual way.
Questions: Does this sound valid? Is there a better way of doing it?
[I'm aware of debate over how best to produce predictions of levels
from log-linear regression, but I'm not sure quite how it applies in
this case.]
Allin Cottrell
_______________________________________________
Gretl-users mailing list -- [email protected]
To unsubscribe send an email to [email protected]
Website:
https://gretlml.univpm.it/postorius/lists/gretl-users.gretlml.univpm.it/
