Absorbing Fixed Effects with estimatr
Source:vignettes/absorbing-fixed-effects.Rmd
absorbing-fixed-effects.Rmd
Whether analyzing a block-randomized experiment or adding fixed
effects for a panel model, absorbing group means can speed up estimation
time. The fixed_effects
argument in both
lm_robust
and iv_robust
allows you to do just
that, although the speed gains are greatest with “HC1” standard errors.
Specifying fixed effects is really simple.
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## hp -0.02403883 0.01503818 -1.598521 0.1211523 -0.05484314 0.006765475 28
lmr_out$fixed_effects
## cyl4 cyl6 cyl8
## 28.65012 22.68246 20.12927
Before proceeding, three quick notes:
- Most of the speed gains occur when estimating “HC1” robust standard errors, or “stata” standard errors when there is clustering. This is because most of the speed gains come from avoiding inverting a large matrix of group dummies, but this step is still necessary for “HC2”, “HC3”, and “CR2” standard errors.
- While you can specify multiple sets of fixed effects, such as
fixed_effects = ~ year + country
, please ensure that your model is well-specified if you do so. If there are dependencies or overlapping groups across multiple sets of fixed effects, we cannot guarantee the correct degrees of freedom. - For now, weighted “CR2” estimation is not possible with fixed_effects.
Speed gains
In general, our speed gains will be greatest as the number of groups/fixed effects is large relative to the number of observations. Imagine we have 300 matched-pairs in an experiment.
# Load packages for comparison
library(microbenchmark)
library(sandwich)
library(lmtest)
# Create matched-pairs dataset using fabricatr
set.seed(40)
library(fabricatr)
dat <- fabricate(
blocks = add_level(N = 300),
indiv = add_level(N = 2, z = sample(0:1), y = rnorm(N) + z)
)
head(dat)
## blocks indiv z y
## 1 001 001 1 1.4961828
## 2 001 002 0 -0.8595843
## 3 002 003 1 0.1709400
## 4 002 004 0 -0.3215731
## 5 003 005 1 -0.3037704
## 6 003 006 0 -1.4214866
# With HC2
microbenchmark(
`base + sandwich` = {
lo <- lm(y ~ z + factor(blocks), dat)
coeftest(lo, vcov = vcovHC(lo, type = "HC2"))
},
`lm_robust` = lm_robust(y ~ z + factor(blocks), dat),
`lm_robust + fes` = lm_robust(y ~ z, data = dat, fixed_effects = ~ blocks),
times = 50
)
## Warning in microbenchmark(`base + sandwich` = {: less accurate nanosecond times
## to avoid potential integer overflows
## Unit: milliseconds
## expr min lq mean median uq max
## base + sandwich 144.83910 157.07768 166.82836 160.43298 170.30547 253.8559
## lm_robust 35.06197 38.02024 42.95171 40.85509 42.98317 119.5000
## lm_robust + fes 22.39199 24.78696 31.87971 25.58353 28.13502 110.9428
## neval cld
## 50 a
## 50 b
## 50 c
Speed gains are considerably greater with HC1 standard errors. This is because we need to get the hat matrix for HC2, HC3, and CR2 standard errors, which requires inverting that large matrix of dummies we previously avoided doing. HC0, HC1, CR0, and CRstata standard errors do not require this inversion.
# With HC1
microbenchmark(
`base + sandwich` = {
lo <- lm(y ~ z + factor(blocks), dat)
coeftest(lo, vcov = vcovHC(lo, type = "HC1"))
},
`lm_robust` = lm_robust(
y ~ z + factor(blocks),
dat,
se_type = "HC1"
),
`lm_robust + fes` = lm_robust(
y ~ z,
data = dat,
fixed_effects = ~ blocks,
se_type = "HC1"
),
times = 50
)
## Unit: milliseconds
## expr min lq mean median uq
## base + sandwich 143.194427 149.93442 163.747177 156.643411 164.854809
## lm_robust 28.567119 30.85180 39.634912 32.509105 35.842774
## lm_robust + fes 2.668075 2.94052 5.707075 3.292075 3.659332
## max neval cld
## 241.76302 50 a
## 151.96744 50 b
## 87.67661 50 c