Hey Peter,
it is not necessary to use "factor(town)". I can just use "town" as the
name of the towns is already numeric.
Am 14.05.2017 um 20:24 schrieb peter dalgaard:
On 14 May 2017, at 10:22 , Tobias Christoph <s3toc...@uni-bayreuth.de> wrote:
Hey David,
when I used your suggested formula: plm( log(revenue) ~ log(supply) +
factor(town) + as.numeric(as.character(year)), data=R_Test_log_Neu) I will get
the same results as without considering town and year in the formula. So this
might not the clue for taking into account a linear trend.
You probably still want a "*" in the model formula.
(It is not obvious to me why a plain factor(town)*year term does not work,
something in the panel data frame setup auto-converts it to a factor? But why
do you need to say factor(town) then?)
-pd
Please find attached the results of str(R_Test_log_Neu):
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 132 obs. of 4 variables:
$ town : num 1 1 1 1 1 1 1 1 1 1 ...
$ year : num 1 2 3 4 5 6 7 8 9 10 ...
$ revenue: num 39.9 43.3 44 43.2 39.1 ...
$ supply : num 1 1 1 1 1 1 35 101 181 323 ...
Hope this is helpful.
Toby
Am 13.05.2017 um 16:40 schrieb David Winsemius:
On May 13, 2017, at 4:07 AM, Tobias Christoph <s3toc...@uni-bayreuth.de>
wrote:
Hey Peter,
thank you. Yes, I want to have "year" in the varibale.
But if I use "*town*year*" as a furmula, R will create new factor
variable with n levels, where n = (num of towns) x (num of years). What
I'm trying to do is create 50 (town x year) variables such that
town1xyear is 1,2,3... when town== 1 and zero otherwise, repeat for
town2xyear, where state == 2, etc.
It is now clear? Sorry for my bad explanations.
I had suggested that you must provide str(R_Test_log_Neu). I'm still suggesting
this would be a good idea.
Since you have not done so, we can only guess at the right course to follow
from your reports of problems and errors. Peter pointed out that the `time`
function was in the 'stats' package (not from plm or elsewhere as I imagined).
You are implying that 'year' is currently a factor value with levels that
appears as the character versions of integers.
You may be able to get closer to what is possible by using:
plm( log(revenue) ~ log(supply) + factor(town) + as.numeric(as.character(year)),
data=R_Test_log_Neu)
This should fix the problem noted by Peter and avoid the potentially incorrect
construction of the desired linear trend.
If you used the interaction operator "*" between 'town' and the numeric version
of 'year' it will give you two sets of coefficients involving 'town'. The first set will
be the mean deviations from the base factor level. The other set will be the differences
in slopes for the time trends for each of the (factored) towns from the overall time
trend/slope. And for your data you wouldbe constructing a saturated model ... as you
observed in your first message (which remains in the copied thread below).
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