R Under development (unstable) (2024-02-25 r85988 ucrt) -- "Unsuffered Consequences"
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> library(lfe)
Loading required package: Matrix
> options(lfe.threads = 1, digits = 5, warn = 1)
> set.seed(655320)
> x <- rnorm(5000, mean = 200)
> x2 <- rnorm(length(x))
> x3 <- rexp(length(x))
> ## create individual and firm
> id <- factor(sample(1500, length(x), replace = TRUE))
> firm <- factor(sample(1300, length(x), replace = TRUE))
> shoe <- factor(sample(100, length(x), replace = TRUE))
> ## effects
> id.eff <- rnorm(nlevels(id))
> firm.eff <- rnorm(nlevels(firm))
> shoe.eff <- rnorm(nlevels(shoe))
> ## left hand side
> y <- x + 0.25 * x2 + 0.5 * x3 + id.eff[id] + firm.eff[firm] + shoe.eff[shoe] + rnorm(length(x))
> 
> ## estimate
> summary(est <- felm(y ~ x + x2 + x3 | id + firm + shoe))

Call:
   felm(formula = y ~ x + x2 + x3 | id + firm + shoe) 

Residuals:
   Min     1Q Median     3Q    Max 
-2.716 -0.422  0.000  0.421  2.208 

Coefficients:
   Estimate Std. Error t value Pr(>|t|)    
x    0.9950     0.0210    47.4   <2e-16 ***
x2   0.2531     0.0211    12.0   <2e-16 ***
x3   0.5182     0.0207    25.0   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.995 on 2185 degrees of freedom
Multiple R-squared(full model): 0.914   Adjusted R-squared: 0.803 
Multiple R-squared(proj model): 0.577   Adjusted R-squared: 0.0313 
F-statistic(full model):8.25 on 2814 and 2185 DF, p-value: <2e-16 
F-statistic(proj model):  992 on 3 and 2185 DF, p-value: <2e-16 
*** Standard errors may be too high due to more than 2 groups and exactDOF=FALSE


> cat("Components:", nlevels(est$cfactor), "largest:", sum(est$cfactor == "1"), "\n")
Components: 7 largest: 4993 
> ## extract the group fixed effects
> for (ef in c("ln", "ref")) {
+   fe <- getfe(est, ef = ef)
+   ## merge back
+ 
+   ideff <- fe[paste("id", id, sep = "."), "effect"]
+   firmeff <- fe[paste("firm", firm, sep = "."), "effect"]
+   shoeeff <- fe[paste("shoe", shoe, sep = "."), "effect"]
+ 
+   ## verify that id and firm coefficients are 1
+   options(scipen = 8)
+   print(summary(lm(y ~ x + x2 + x3 + ideff + firmeff + shoeeff - 1), digits = 8))
+ }
Warning in is.estimable(ef, obj$fe) :
  non-estimable function, largest error 0.3 in coordinate 184 ("id.198")
Warning in getfe.kaczmarz(obj, se, ef = ef, bN = bN, robust = robust, cluster = cluster,  :
  Supplied function seems non-estimable

Call:
lm(formula = y ~ x + x2 + x3 + ideff + firmeff + shoeeff - 1)

Residuals:
   Min     1Q Median     3Q    Max 
-2.716 -0.422  0.000  0.421  2.208 

Coefficients:
         Estimate Std. Error t value Pr(>|t|)    
x       0.9949750  0.0000765 13004.6   <2e-16 ***
x2      0.2530979  0.0093197    27.2   <2e-16 ***
x3      0.5182466  0.0090240    57.4   <2e-16 ***
ideff   1.0000000  0.0079960   125.1   <2e-16 ***
firmeff 1.0000000  0.0078187   127.9   <2e-16 ***
shoeeff 1.0000000  0.0107776    92.8   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.658 on 4994 degrees of freedom
Multiple R-squared:     1,	Adjusted R-squared:     1 
F-statistic: 7.73e+07 on 6 and 4994 DF,  p-value: <2e-16


Call:
lm(formula = y ~ x + x2 + x3 + ideff + firmeff + shoeeff - 1)

Residuals:
   Min     1Q Median     3Q    Max 
-2.716 -0.422  0.000  0.421  2.208 

Coefficients:
        Estimate Std. Error t value Pr(>|t|)    
x       0.994975   0.000123  8061.2   <2e-16 ***
x2      0.253098   0.009320    27.2   <2e-16 ***
x3      0.518247   0.009024    57.4   <2e-16 ***
ideff   1.000000   0.007997   125.0   <2e-16 ***
firmeff 1.000000   0.007816   127.9   <2e-16 ***
shoeeff 1.000000   0.010777    92.8   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.658 on 4994 degrees of freedom
Multiple R-squared:     1,	Adjusted R-squared:     1 
F-statistic: 7.73e+07 on 6 and 4994 DF,  p-value: <2e-16

> 
> # Perform a bootstrap
> a <- felm(y ~ x + x2 + x3 | id + firm, nostats = TRUE, Nboot = 10, bootexpr = quote(x / x3 * x2))
> mean(a$boot)
[1] 0.55391
> sd(a$boot)
[1] 0.10028
> 
> proc.time()
   user  system elapsed 
   1.96    0.14    2.09