[R] 'plm' package problem

Hi, I ran 3 plm regressions (fixed effects) : twoways, individual and time effects are added. I noticed that I am getting the same results in each model. I expect the results to be different given the varying effects introduced in each model. Can you please help sort this out, please? or Am I ru

[R] Panel Granger Non-Causality Tests in R

Hi, I was wondering if there is a package/function for Panel Granger non-causality tests? I am interested in Toda-Yamamoto like procedure for panel models. Thank you, -- View this message in context: http://r.789695.n4.nabble.com/Panel-Granger-Non-Causality-Tests-in-R-tp4666316.html Sent fr

[R] Panel Granger Causality Tests

Hi, I was wondering if there is a package/function for Panel Granger non-causality tests? I am interested in Toda-Yamamoto procedure in panel data setting. Thank you, -- View this message in context: http://r.789695.n4.nabble.com/Panel-Granger-Causality-Tests-tp4665834.html Sent from the R h

Re: [R] ZA unit root test lag order selection

Dear Matthieu, Many thanks for your reply. I was not sure what the best way forward in selecting lag length. Eventually I wrote a function that carries out serial correl test and AIC based lag length selections. I used "urca" package. Here is what I come up with in the end: >zamod.A=ur.za(x, mod

[R] ZA unit root test lag order selection

I was wondering if anyone could help with choosing optimal lag length for ZA test. There have been two lag order selection methods commonly used in the literature: 1) The ZA paper recommends to run the test with maximum number of lags. Then the lag order is reduced sequentially until the longest

[R] sorting the VAR model output according to variable names??

I was wondering if one can have the coefficients of VAR model sorted according to variable names rather than lags. If you notice below, the output is sorted according to lags. >VAR(cbind(fossil,labour),p=2,type="const") VAR Estimation Results: === Estimated coefficients for

[R] plm package, random effects model error message

Hello, I am trying to run the following Random Effects panel model in R, but I am getting an error message. I am just thinking, maybe the random.method I chose is not working? re1=plm(gdp ~ fossil+renewables+labour+gfcf, model = "random", data = new.frame,index = c("id"),random.method = "swar") E

[R] plm: Hausman Test error

Hi, I am trying to run a panel regression using 88 observations and 9 variables. In-built Hausman Test did not work, then I found a code for auxiliary regression method for the Hausman test. The panel models are: fe=plm(gd ~ l+g+o+c+g1+h+n+r, model = "within", data = new.frame,index = c("id")) r

[R] panel model hausman test

Hi, I am trying to run panel regression using Fixed and Random effects models. I am getting the following error message after running the hausman test >Fmodel=plm(gdp ~ labour+gfcf+oil+coal+gas+hydroel+nuclear+rnwable, model = "within", data = new.frame,index = c("id")) >Rmodel=plm(gdp ~ lab

Re: [R] R on mac not installing packages

Many thanks to all who replied to this post. the problem has now been sorted out. i installed "GCC-10.7-v2.pkg", now i can install packages from source -- View this message in context: http://r.789695.n4.nabble.com/R-on-mac-not-installing-packages-tp4659392p4659414.html Sent from the R help ma

[R] R on mac not installing packages

Hi, I have not been able to use R in my macbook pro. I am getting the following error message every time i try to install a package * installing *source* package ‘Hmisc’ ... ** package ‘Hmisc’ successfully unpacked and MD5 sums checked ** libs *** arch - i386 sh: make: command not found ERROR: co

[R] package installation error in Mac OS X

Hi, I installed R in Mac OS X, and trying to installa package. R is not allowing me to install the meboot package. Below is the exact message I got from R: installation of package ‘meboot’ had non-zero exit status trying URL 'http://cran.ma.imperial.ac.uk/src/contrib/meboot_1.1-5.tar.gz' Content

[R] VAR simulation help

Hi Everyone, I am a new comer to R circle. I am trying to simulate a VAR estimation. The problem is given above in the image. Assume that the errors are iid and normally distributed. Here the number of observations x1=x2=64. I've writ

[R] set.seed()

Hi, I am learning R. I've been using set.seed() for a while, but without actually understanding the significance of the "number" we put in the brackets. e.g. set.seed(135) & set.seed(930). Can anyone shed some light on this please? -- View this message in context: http://r.789695.n4.nabble.co

[R] meboot

Hi, I hope there is someone to help me with using 'meboot' package. I tried to create an ensemble of 999 replicates of a vector. however, I am getting the following error > oil.ens <- meboot(x = panel, reps = jboot, colsubj = 1, coldata = 3) Error in `[.data.frame`(x, order(x, na.last = na.last,