I am trying to conduct a comparison of coefficient tests across two subsamples. To achieve this, I do the following:
full_model <- lm(y ~ v1*subsample_dummy + fixed_effects, data=df)
reduced_model <- lm(y ~ v1 + subsample_dummy + fixed_effects, data=df)
test <- anova(full_model, reduced_model)
The above gives me the result.
However, I am not sure how to do the same in the situation where I have to cluster the models by, let's say, the year variable.
I can cluster the lm models using the following code:
library(sandwich)
# cluster by year
clustered_se <- vcovCL(full_model, ~ year)
clustered_se1 <- vcovCL(reduced_model, ~ year)
# generate summaries with clustered standard errors
a <- coeftest(full_model, vcov. = clustered_se)
b <- coeftest(reduced_model, vcov. = clustered_se1)
However, the issue remains, as I still cannot do:
anova(a, b)
How to achieve the comparison of coefficient test across subsamples when the model requires standard error clustering?
We can use sandwich::vcovCL to get essentially the same standard errors like lfe::felm. Let's estimate some models.
> est1 <- lfe::felm(y ~ x1 + x2 | id + firm | 0 | firm, data=d) ## id + firm FE, clustered by firm
> est2 <- lfe::felm(y ~ x1 | id + firm | 0 | firm, data=d) ## same, restricted model
> est3 <- lm(y ~ x1 + x2 + as.factor(id) + as.factor(firm), data=d) ## as est1 w/o clustering
> est4 <- lm(y ~ x1 + as.factor(id) + as.factor(firm), data=d) ## as restricted est3
Comparing the standard errors of est3 with est1,
> lmtest::coeftest(est3, vcov.=\(x)
+ sandwich::vcovCL(x, cluster=d$firm, type='HC0'))[1:3, ]
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7416642 0.10554929 35.44945 5.454680e-177
x1 1.0432612 0.02965723 35.17730 3.631800e-175
x2 0.4904104 0.03186679 15.38939 5.822822e-48
> coef(summary(est1))
Estimate Cluster s.e. t value Pr(>|t|)
x1 1.0432612 0.02968696 35.14207 1.799173e-13
x2 0.4904104 0.03189874 15.37398 2.931632e-09
yields essentially the same.
Thus, and according to a post on Cross validated [1] we could compare models est1 and est2 using an lmtest::waldtest (there's also a lfe::waldtest which works differently).
> lmtest::waldtest(est3, est4, vcov=\(x)
+ sandwich::vcovCL(x, cluster=d$firm, type='HC0'))
Wald test
Model 1: y ~ x1 + x2 + as.factor(id) + as.factor(firm)
Model 2: y ~ x1 + as.factor(id) + as.factor(firm)
Res.Df Df F Pr(>F)
1 966
2 967 -1 236.83 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hope this brings you a step further.
BTW: You definitely could file a feature request on this as an issue on the author's GitHub.
Data:
> set.seed(42)
> n <- 1e3
> d <- data.frame(x1=rnorm(n), x2=rnorm(n), id=factor(sample(20, n, replace=TRUE)),
+ firm=factor(sample(13, n, replace=TRUE)), u=rnorm(n))
> id.eff <- rnorm(nlevels(d$id))
> firm.eff <- rnorm(nlevels(d$firm))
> d$y <- d$x1 + 0.5 * d$x2 + id.eff[d$id] + firm.eff[d$firm] + d$u