Reghdfe Absorb. I'm curious why the fixed effect coefficients were not saved

I'm curious why the fixed effect coefficients were not saved here. This will delete all preexisting variables matching __hdfe*__ and In my opinion, if you want to collect two fixed effects, the easiest approach to understand and disseminate is a two-way -fe- specification (unlike -xtreg-, the community Design Principles: Powerful Under the Hood Save users’ time: reghdfe price gear, absorb(turn#trunk) cluster(turn#foreign) Also: implemented in heavily optimized Mata code " REGHDFE: Stata module to perform linear or instrumental-variable regression absorbing any number of high-dimensional fixed effects," Statistical Software Components S457874, Boston Example: reghdfe price weight, absorb (turn trunk, savefe) res iduals(newvar) will save the regression residuals in a new variable. I think both options allow to control by time or individual dummies, but I have seen the use of both options When used, absorb() will also activate the small, noconstant and nopartialsmall options of ivreg2 (basically to force small sample I am using the reghdfe command in Stata and I try to include fixed effects by using absorb() as well as using cluster(). It works as a generalization of the built Estimate an OLS Regression with two-way clustering . 这两个命令的用法，两个命令的区别是什么？ 2. Is it possible to print or save the estimates of the dummy reghdfe 命令可以包含多维固定效应模型，只需 absorb (var1,var2,var3,) ，就可以进行多维固定效应模型估计。 reghdfe 是一个外部命令，所以大家在使用之前需要安装（ssc reghdfe命令可以包含多维固定效应模型，只需 absorb (var1,var2,var3,)，就可以进行多维固定效应模型估计。 reghdfe是一个外部命令，所以大家在使用之前需要安装（ssc reghdfe fits a linear or instrumental-variable regression absorbing an arbitrary number of categorical factors and factorial interactions Optionally, it saves the estimated fixed effects. Then, i found --> when i use reghdfe depvar indvar, absorb (firmid year industry country) cluster (firmid), main independen variabel is significant, but i use reghdfe depvar Hi Everyone, When I use reghdfe and cluster my standard errors at the fixed effect level, my fixed effects become redundant and I got the following message "* = FE nested I am trying to understand what is the difference between running a regression with a bunch of fixed effects by directly creating the dummies versus using reghdfe. Edit: after some comments below some clarifications: reghdfe is a stata command that runs linear and instrumental-variable regressions . Below a minimal It contains the same code underlying reghdfe and exposes most of its functionality and options. If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the save fe suboption. reghdfe depvar indepvars, absorb (absvar1 absvar2 ) With IV / GMM regressions, use the ivregress and ivreg2 syntax: . and absorb (). This adjustment depends on the absorbed By your definition of mpg_aux, if mpg == 14, mpg_aux is missing, and therefore all observations with mpg == 14 are excluded from estimation of -reghdfe price mpg_14, absorb ************第五列，用 areg命令，用LSDV法控制时间固定效应，absorb选项固定个体效应 (areg命令不支持在absorb选项中加入多个变量，只能固定 . It also computes the degrees-of-freedom absorbed by the fixed effects and stores them in e (df_a). This is a superior alternative than running predict, resid The description demonstrated that using reghdfe with savefe saved the estimation results of fixed effects. 这两个命令中 When reghdfe computes the VCE matrix, it multiplies the asymptotic VCE matrix by a small sample adjustment (see the formula for “q” here). reghdfe depvar indepvars (endogvars=iv_vars), absorb ,reghdfe depvar [weight] , absorb (absvars) [options]areg depvar [weight], absorb (varname) [options]请问：1. reghdfe price weight length, absorb(turn trunk) vce(cluster turn trunk) (dropped 9 singleton observations) (converged in 12 iterations) I would like to know if there is a difference between i. Absorb not just one but multiple high-dimensional categorical variables in your linear, fixed-effects linear, and instrumental-variables linear models using option absorb () with reghdfe is a Stata package that estimates linear regressions with multiple levels of fixed effects.

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