search for: xtreg

I'm trying to replicate a time-series cross-sectional analysis (countries over years) with SEs clustered by country. ?The original analysis was done in Stata 10 with: xtreg [DV] [IVs] fe cluster(country). Using plm() in R (cran.r-project.org/web/packages/plm/index.html), I've replicated the coefficients. I sought to estimate country-clustered SEs with vcovHC(), and tried a variety of options, but couldn't exactly replicate the published (i.e., Stata 10's)...

i want to ask 2 questions. 1) can R do Random-effects GLS regression which i can get from Stata? the following result is frome Stata.can I get the alike result from R? xtreg lwage educ black hisp exper expersq married union, re Random-effects GLS regression Number of obs = 4360 Group variable (i) : nr Number of groups = 545 R-sq: within = 0.1799 Obs per group: min = 8...

Dear all, How can one fit just a fixed effects model in R? lme() seems to require a random component, whereas I am just looking for something like STATA's "xtreg, fe" function. Thanks, Pijus ---- W: 212-588-7897 F: 212-446-1955 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe&...

Dear R-list, I am currently working on a dataset with a colleague who uses stata. We fit a random intercept model to the data (decisions clustered in participants) and get closely the same results in stata (using xtreg re) and R (using the lme4 or multilevel package). Now in stata, there is an additional option for the regression to control for clustering; the vce(cluster clustvar) option, which changes the standard errors quite a bit. (see http://www.stata.com/support/faqs/statistics/standard-errors-and-vce-cl...

Hi everyone, I have tried to solve a simple problem for days but I can't figure out how to run it properly. If someone could give me a hint, this would be really great. Basically, I want to run a standard economist's fixed, and random effects regression (corresponds to xtreg in STATA) but with _variable_ weights (they correspond to changing industry shares in the market). Here is what I do: regsc<-lme(dsc~dcomp+dperc,random=~1|ind7090) update(regsc,weights=varFixed(~wt)) 1. however, my results are different from what I obtain in Stata using areg (the weighted f...

...~ 1|Subject, method="ML", data = foo, weight=~w) does not maximize the likelihood Sum_i {w_i * logLik_i}, since 'weight' describes the with-in heteroscedasticity structure. I think I need something like the option 'iweight' (importance weight) for the command 'xtreg' of Stata. Any suggestion for R? Thanks in advance, Marco Geraci > sessionInfo() R version 2.2.1, 2005-12-20, i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils"...

...list has received it... and I need help (if you have time)! ;-) I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by individuals. For those using Stata, the counterpart would be xtreg with the "fe" option, or areg with the "absorb" option and in both case the clustering is achieved with "vce(cluster id)" My question is : how could I do that with R ? An important point is that I have too many individuals, therefore I cannot include dummies and shou...

Dear R-helpers, I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by individuals. For those using Stata, the counterpart would be xtreg with the "fe" option, or areg with the "absorb" option and in both case the clustering is achieved with "vce(cluster id)" My question is : how could I do that with R ? An important point is that I have too many individuals, therefore I cannot include dummies and shoul...

...--------------------------------------------- inno | 146574 .0880374 .2833503 0 1 lnE | 146353 .9256239 1.732912 -4.473922 10.51298 lnM | 146209 4.281903 1.862192 -4.847253 13.71969 2. Estimate model . xi: xtreg lnLP lnC lnL lnE lnM eco inno eco_inno eco_lnE eco_lnM i.year, fe i(stno) i.year _Iyear_1997-1999 (naturally coded; _Iyear_1997 omitted) Fixed-effects (within) regression Number of obs = 146167 Group variable (i): stno Number of groups...

...------------------------------------ inno | 146574 .0880374 .2833503 0 1 lnE | 146353 .9256239 1.732912 -4.473922 10.51298 lnM | 146209 4.281903 1.862192 -4.847253 13.71969 2. Estimate model . xi: xtreg lnLP lnC lnL lnE lnM eco inno eco_inno eco_lnE eco_lnM i.year, fe i(stno) i.year _Iyear_1997-1999 (naturally coded; _Iyear_1997 omitted) Fixed-effects (within) regression Number of obs = 146167 Group variable (i): stno Number of g...

...on a theoretically-relevant reduced dataset -- that is, if I drop some observations (for committees nobody would ever care about), it behaves again. What I'm wondering is: (1) Is the model basically running home to OLS-or-very-close-to-it? If I estimate the same model using stata's xtreg, it returns a sigma_u of zero, and if I estimate it with HLM, it generates a bad tau and tries again with one that is positive but weensy. Are the algorithms in lme doing the same thing here, or something closely analogous? Generating an impermissible negative that gets truncat...

...bles are id for each individual, year, age (and age squared to age quartic) and (to begin with) constant weights for each individual. I would prefer to use yearly changing weights per individual to capture better attrition. Do my weights have to be constant in nlme for every individual (just as in xtreg in Stata)? My main variables: id year income age cross-sectional weight longitudinal weight Are you aware of any other packages in R which provide the opportunity to examine longitudinal data with sample weights? Kind regards, Arne [[alternative HTML version deleted]]

Dear R Users, Here is another probelm/question. I would like to run some panel regressions with R. Therefore I have combined several time periods of data for different individuals in my database. I have already run pooled OLS but I would need to calculate a Fixed Effects Estimator (within estimator). Unfortunately I couldn't find anything like that in the RSearch and I suppose that lme

...1 > > > > lnE | 146353 .9256239 1.732912 -4.473922 10.51298 > > > > lnM | 146209 4.281903 1.862192 -4.847253 13.71969 > > > > > > > > 2. Estimate model > > > > > > > > . xi: xtreg lnLP lnC lnL lnE lnM eco inno eco_inno eco_lnE eco_lnM i.year, fe i(stno) > > > > i.year _Iyear_1997-1999 (naturally coded; _Iyear_1997 omitted) > > > > > > > > Fixed-effects (within) regression Number of obs = 146167 > >...