Hi Rodrigo,
Thanks for your info.
Sorry that I did not describe my model clearly. My
model use interaction between ids and time trend (not
with years).
Therefore, with 5 provinces have 19 data points and
other 5 provinces has 14 data points, use price,
umeployment rate, income, ids dummies, year dummies
and provinical specific trends as regressors, the
degree of freedom is NT- 3 covariates - 9 ids dummies
- 18 year dummies - 9 provinical specific trends =126.
This is what I got when I coded the trend variable as
Way II (below) and run the regression. Initially, I
put in 9 ids dummies, 18 year dummies and 10
provinical-specific trends. However, Stata
automatically drop one of the provinical-specfic
trends.
It is a good suggestion to impute the data and use all
obs for regression. I may try it. That said, I still
want to know the answer to my question. The price
coefficients that I got from using Way I and Way II
coding of the provinical-specific trends differs quite
a lot. Hence, I want to know what is the correct way
to code.
Thanks again for your valuable ideas.
Rita Luk
--- "Rodrigo Alfaro A." <[email protected]> wrote:
>
> Rita,
>
> Suppose that you have a way to deal with your
> "missings at random". In
> that scenario you have 10 ids and 19 years
> (obs=190), and you are
> estimating the model with ids and year dummies,
> other covariates, and
> interactions between ids and years. How many degree
> of freedom you have?
>
>
> If you think that the missings are at random, maybe
> you could deal with
> them using a multiple imputation procedure, such as
> the one available in
> -ice-. You could be in the scenario above.
>
> I do not know what is the purpose of your model, but
> from time-series
> point of view it could be nice to have a long serie,
> then keep the
> provinces for the full sample sounds a good idea. On
> the other hand,
> keep the complete sample gives you 5 years and 10
> ids... which is few
> obs but maybe enough for your goal.
>
> Rodrigo.
>
>
>
>
> -----Mensaje original-----
> De: [email protected]
> [mailto:[email protected]] En
> nombre de rita luk
> Enviado el: Martes, 15 de Abril de 2008 12:09 p.m.
> Para: [email protected]
> Asunto: st: Time trend in fixed effects modle
>
> Hi Statalist,
>
> Have a question on the design matrix of a fixed
> effects model.
>
> I have cigarette sales and price data for a panel of
> ten provinces. Some
> provinces have data for 19 years
> (1981-1999) while other have data for 14 years
> (1981-1989, 1995-1999.
> missing at random).
>
> I regress sales on price, other provincial level
> covariates, provincial
> and year dummy variables, and provincial specific
> time trends. My
> question is on the last item.
>
> To create provincial specific time trends variables,
> I create a time
> trend variable, and interact it with provincial
> dummy variables. For a
> province with full data, value of the trend variable
> are 1, 2, 3,... 19
> corresponding to years 1981 to 1999.
>
> For a province with only 14 data points, the trend
> value for 1981 to
> 1989 are the same as above. However, I do not know
> the proper way for
> values of 1995 and thereafter. I can think of 2
> possible ways:
>
> Year Way I Way II
> 1981 1 1
> 1982 to 1988 skip here skip
> 1989 9 9
> (1990-1994) missing data missing data
> 1995 15 10
> 1996 16 11
> 1997 17 12
> 1998 18 13
> 1999 19 14
>
> Which way of coding is correct? Does the coding
> depend on the research
> question in hand? I have searched on some fixed
> effects model with
> unbalanced panel and unequal spacing, and found
> nothing said on this.
>
> Thanks very much for all of your help.
>
>
> Rita Luk
> Research Officer
> Ontario Tobacco Research Unit
> University of Toronto
> Toronto, Ontario, Canada
> (416) 535-8501 x4727
>
>
>
>
>
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