Dang! That's awesome!!!!! Being at the end of an empirical PhD in which all the econometrics was done in R, I was already a longtime R enthusiast, but you never stop learning more neat features!!!
YAY to everyone involved in R's development!!!! Toby Adaikalavan Ramasamy wrote: > You will need to ensure that firm is a factor and not numerical (i.e. > continuous). Here is an example > > > firm <- factor( sample(1:3, 20, replace=T) ) > x1 <- runif(20) > y <- rnorm(20) > > summary( fit <- lm( y ~ -1 + x1 + firm ) ) > ... > Coefficients: > Estimate Std. Error t value Pr(>|t|) > x1 -0.04964 0.74861 -0.066 0.948 > firm1 0.10732 0.48269 0.222 0.827 > firm2 0.27548 0.48781 0.565 0.580 > firm3 -0.07651 0.53384 -0.143 0.888 > > NB : The "-1" in the formula forces each firm to have its own intercept. > > > Use model.matrix, you will see the dummy variables created within lm(). > > model.matrix( fit ) > x1 firm1 firm2 firm3 > 1 0.6641647 0 1 0 > 2 0.5142712 1 0 0 > 3 0.2197956 1 0 0 > 4 0.3211675 0 1 0 > 5 0.1892449 1 0 0 > 6 0.7740754 0 0 1 > 7 0.3486932 0 1 0 > 8 0.2116816 0 0 1 > 9 0.2426825 0 1 0 > 10 0.2219768 1 0 0 > 11 0.9328514 1 0 0 > 12 0.7880405 0 0 1 > 13 0.8673492 0 1 0 > 14 0.1777998 0 1 0 > 15 0.3178498 1 0 0 > 16 0.3379726 0 0 1 > 17 0.9193359 1 0 0 > 18 0.6998152 0 1 0 > 19 0.2825702 0 0 1 > 20 0.6139586 1 0 0 > > Regards, Adai > > > > On Mon, 2005-09-05 at 15:53 +0100, Tobias Muhlhofer wrote: > >>So are you guys saying to me that if I have variable firm which is the >>factor of all firm identifiers, I could just go >> >>lm(y ~ x + firm) >> >>and that will implicitly include a dummy for each level of factor firm, >>thus making this a fixed effects (aka LSDV) model? >> >>T >> >> >>Jean Eid wrote: >> >>>You can turn the identity vector of the firms into a factor and do lm .... >>> >>>Jean >>> >>>On Mon, 5 Sep 2005, Tobias Muhlhofer wrote: >>> >>> >>> >>>>Hi, all! >>>> >>>>Anyone know an easy way to specify the following model. >>>> >>>>Panel dataset, with stock through time, by firm. >>>> >>>>I want to run a model of y on a bunch of explanatory variables, and one >>>>dummy for each firm, which is 1 for observations that come from firm i, >>>>and 0 everywhere else. I have over 200 firms (and a factor variable that >>>> contains a firm identifier). >>>> >>>>Any easy way of going about this, without having to define all these >>>>dummies? I checked lme() with random = ~ 1|firm, but the problem is that >>>>these are random effects, i.e. that there are firm-by-firm disturbance >>>>terms and overall disturbance terms, whereas I want just overall >>>>disturbance terms. This is generally called a "fixed effects" model, >>>>although it seems like the term "fixed effects" is being used somewhat >>>>differently in the context of the nlme package. >>>> >>>>Toby >>>> >>>>-- >>>>************************************************************************** >>>>When Thomas Edison invented the light bulb he tried over 2000 >>>>experiments before he got it to work. A young reporter asked >>>>him how it felt to have failed so many times. He said >>>>"I never failed once. I invented the light bulb. >>>>It just happened to be a 2000-step process." >>>> >>>>______________________________________________ >>>>[email protected] mailing list >>>>https://stat.ethz.ch/mailman/listinfo/r-help >>>>PLEASE do read the posting guide! >>>>http://www.R-project.org/posting-guide.html >>>> >>> >>> >>> > > -- ************************************************************************** When Thomas Edison invented the light bulb he tried over 2000 experiments before he got it to work. A young reporter asked him how it felt to have failed so many times. He said "I never failed once. I invented the light bulb. It just happened to be a 2000-step process." ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
