This is a function advanced users which allows to estimate any fixest estimation from a fixest environment obtained with only.env = TRUE in a fixest estimation.

est_env(env)

## Arguments

env

An environment obtained from a fixest estimation with only.env = TRUE. This is intended for advanced users so there is no error handling: any other kind of input will fail with a poor error message.

## Value

It returns the results of a fixest estimation: the one that was summoned when obtaining the environment.

## Details

This function has been created for advanced users, mostly to avoid overheads when making simulations with fixest.

How can it help you make simulations? First make a core estimation with only.env = TRUE, and usually with only.coef = TRUE (to avoid having extra things that take time to compute). Then loop while modifying the appropriate things directly in the environment. Beware that if you make a mistake here (typically giving stuff of the wrong length), then you can make the R session crash because there is no more error-handling! Finally estimate with est_env(env = core_env) and store the results.

Instead of est_env, you could use directly fixest estimations too, like feols, since they accept the env argument. The function est_env is only here to add a bit of generality to avoid the trouble to the user to write conditions (look at the source, it's just a one liner).

Objects of main interest in the environment are:

• lhsThe left hand side, or dependent variable.

• linear.matThe matrix of the right-hand-side, or explanatory variables.

• iv_lhsThe matrix of the endogenous variables in IV regressions.

• iv.matThe matrix of the instruments in IV regressions.

• weights.valueThe vector of weights.

I strongly discourage changing the dimension of any of these elements, or else crash can occur. However, you can change their values at will (given the dimension stay the same). The only exception is the weights, which tolerates changing its dimension: it can be identical to the scalar 1 (meaning no weights), or to something of the length the number of observations.

I also discourage changing anything in the fixed-effects (even their value) since this will almost surely lead to a crash.

Note that this function is mostly useful when the overheads/estimation ratio is high. This means that OLS will benefit the most from this function. For GLM/Max.Lik. estimations, the ratio is small since the overheads is only a tiny portion of the total estimation time. Hence this function will be less useful for these models.

Laurent Berge

## Examples


# Let's make a short simulation
# Inspired from Grant McDermott bboot function

# Simple function that computes a Bayesian bootstrap
bboot = function(x, n_sim = 100){
# We bootstrap on the weights
# Works with fixed-effects/IVs
#  and with any fixest function that accepts weights

core_env = update(x, only.coef = TRUE, only.env = TRUE)
n_obs = x\$nobs

res_all = vector("list", n_sim)
for(i in 1:n_sim){
assign("weights.value", rexp(n_obs, rate = 1), core_env)
res_all[[i]] = est_env(env = core_env)
}

do.call(rbind, res_all)
}

est = feols(mpg ~ wt + hp, mtcars)

boot_res = bboot(est)
coef = colMeans(boot_res)
std_err = apply(boot_res, 2, sd)

# Comparing the results with the main estimation
coeftable(est)
#>                Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept) 37.22727012 1.59878754 23.284689 2.565459e-20
#> wt          -3.87783074 0.63273349 -6.128695 1.119647e-06
#> hp          -0.03177295 0.00902971 -3.518712 1.451229e-03
#> attr(,"type")
#> [1] "IID"
cbind(coef, std_err)
#>                    coef     std_err
#> (Intercept) 37.06416839 1.878346352
#> wt          -3.82426295 0.613391938
#> hp          -0.03262888 0.006218552