> 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). >> >> > -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
