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.
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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