lfe

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Erlang was already installed: $dpkg -l|grep erlang ii erlang 1:13.b.3-dfsg-2ubuntu2 Concurrent, real-time, distributed function ii erlang-appmon 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP application monitor ii erlang-asn1 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP modules for ASN.1 support ii erlang-base 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP virtual machine and base applica ii erlang-common-test 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP application for automated testin ii erlang-debugger 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP application for debugging and te ii erlang-dev 1:13.b.3-dfsg-2ubuntu2 Erlang/OTP development libraries

When I run a cluster standard error panel specification with plm and lfe I get results that differ at the second significant figure. Does anyone know why they differ in their calculation of the SE's? set.seed(572015) library(lfe) library(plm) library(lmtest) # clustering example x <- c(sapply(sample(1:20), rep, times = 1000)) + rnorm(20*1000, sd = 1) y <- 5 + 10*x + rnorm(20*1000, sd = 10) + c(sapply(rnorm(20, sd = 10), rep, times = 1000)) facX <- factor(sapply(1:20, rep, times = 1000)) mydata <- data.frame(y=y,x=x,facX=facX, state=rep(1:1000, 20)) model <- plm(y ~ x, data = mydata, index = c(

Does anyone have a nice clean way to get predict behavior for felm models? library(lfe) model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species) predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Works model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species) predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Does not work As a workaround, you could combine felm , getfe , and demeanlist as follows: library(lfe) lm.model <- lm(data=demeanlist(iris[, 1:2], list(iris$Species)), Sepal.Length ~ Sepal.Width) fe <- getfe(felm

Is there way to get predict behavior with standard errors from lfe::felm if the fixed effects are swept out using the projection method in felm ? This question is very similar to the question here , but none of the answers to that question can be used to estimate standard errors or confidence/prediction intervals. I know that there's currently no predict.felm , but I am wondering if there are workarounds similar to those linked above that might also work for estimating the prediction interval library(DAAG) library(lfe) model1 <- lm(data = cps1, re74 ~ age + nodeg + marr) predict(model1,