2022年12月3日 · I'm using the felm function in R for time-fixed effects but I'm having trouble specifying my model. I tried the following two but they result into completely different outputs. Could you explain what does the 0 stand for? Which of the following models would be correct for my analysis? My sample consists of 400 observations (40 countries over 10 ...

2018年12月18日 · Therefore I explored the R-package lfe. It provides the function felm which “absorbs” factors (similar to Stats’s areg). I need to use robust standard errors (HC1 or so) since tests indicate that there might be heteroscedasticity. This is not so flamboyant after all. However, I obtain odd results for the robust SEs (using felm and huxreg).

2015年4月1日 · lm R-sq: not good for Fixed Effects model, cannot reproduce; lfe "full" R-sq: R-sq for all pairs predicted y and original y, may also be called as "overall" R-sq; lfe "proj" R-sq: "within" R-sq: how much of the variation in the dependent variable within each entity group is captured by the model; plm model="within" R-sq: same as 3.

2020年6月30日 · For these regressions, I would like to cluster the standard errors by several dimensions (eg. product, destination and time). However, I'm confused about the syntax and how it differs between the felm and feols commands. Would the …

here is a quick interpretation- overall r squared- the total amount of variance explained by your model within r squared- the total amount of variance explained by your conditional predictors (your random effects) between r squared- the total amount of variance explained by your nominal predictors (your fixed effects)

2020年8月28日 · The coefficients and standard errors generated by the code look the same in both approaches, but the two commands give me different F statistics, even while assuming the same degrees of freedom. ivreghdfe provides much more detail on which F statistics are being calculated and provides a number of ways to test instrument strength, but none of ...

2018年3月19日 · "The careful reader has noticed that the behaviour of summary() on a ’felm’ object with respect to degrees of freedom and R2 is the same as that of on an ’lm’ object when including an intercept. There is no explicit intercept in the result of felm(), but the factor structure includes one implicitly." Thank you!

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Both are part of the standard R installation. If you mean something else, you need to clarify what you want. I see from this answer that apparently economists use 'fixed effect model' to refer to a conditional logit model, even though it's far from the only fixed effect model involving a logit.

Update on 4/27/2015 to add code from blog post.. api=read.csv("api.csv") #create the variable api from the corresponding csv attach(api) # attach of data.frame objects api1=api[c(1:6,8:310),] # one missing entry in row nr. 7 modell.api=lm(API00 ~ GROWTH + EMER + YR_RND, data=api1) # creation of a simple linear model for API00 using the regressors Growth, Emer and Yr_rnd. ##creation of the ...