R Under development (unstable) (2024-02-23 r85978 ucrt) -- "Unsuffered Consequences"
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> library(lfe)
Loading required package: Matrix
> options(lfe.threads=2,digits=5,warn=1)
> set.seed(65318)
> x <- rnorm(500)
> x2 <- rnorm(length(x))
> 
> ## create individual and firm
> id <- factor(sample(10,length(x),replace=TRUE))
> firm <- factor(sample(6,length(x),replace=TRUE,prob=c(2,rep(1,5))))
> 
> ## effects
> id.eff <- rnorm(nlevels(id))
> firm.eff <- rnorm(nlevels(firm))
> 
> ## left hand side
> y <- x + 0.25*x2 + id.eff[id] + firm.eff[firm] + rnorm(length(x))
> x[sample(500,20)] <- NA
> y[sample(500,20)] <- NA
> ## estimate
> summary(est <- felm(y ~ x+x2 | id+firm))

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

Residuals:
   Min     1Q Median     3Q    Max 
-3.310 -0.660 -0.022  0.743  2.643 

Coefficients:
   Estimate Std. Error t value Pr(>|t|)    
x    0.9702     0.0507   19.12  < 2e-16 ***
x2   0.3254     0.0506    6.43  3.3e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 443 degrees of freedom
  (40 observations deleted due to missingness)
Multiple R-squared(full model): 0.791   Adjusted R-squared: 0.783 
Multiple R-squared(proj model): 0.486   Adjusted R-squared: 0.468 
F-statistic(full model): 105 on 16 and 443 DF, p-value: <2e-16 
F-statistic(proj model):  210 on 2 and 443 DF, p-value: <2e-16 


> getfe(est)
          effect obs comp   fe idx
id.1   -1.636089  40    1   id   1
id.2    0.042766  46    1   id   2
id.3   -0.733947  47    1   id   3
id.4   -0.855250  46    1   id   4
id.5    2.092365  32    1   id   5
id.6    1.123941  52    1   id   6
id.7    1.429550  50    1   id   7
id.8   -0.918586  43    1   id   8
id.9   -2.481822  51    1   id   9
id.10  -0.346876  53    1   id  10
firm.1  0.000000 120    1 firm   1
firm.2 -0.844583  74    1 firm   2
firm.3  0.675597  78    1 firm   3
firm.4  0.748129  64    1 firm   4
firm.5 -1.542114  55    1 firm   5
firm.6  2.193690  69    1 firm   6
> summary(lm(y ~ x + x2 + id + firm -1))

Call:
lm(formula = y ~ x + x2 + id + firm - 1)

Residuals:
   Min     1Q Median     3Q    Max 
-3.310 -0.660 -0.022  0.743  2.643 

Coefficients:
      Estimate Std. Error t value Pr(>|t|)    
x       0.9702     0.0507   19.12  < 2e-16 ***
x2      0.3254     0.0506    6.43  3.3e-10 ***
id1    -1.6361     0.1786   -9.16  < 2e-16 ***
id2     0.0428     0.1705    0.25    0.802    
id3    -0.7339     0.1753   -4.19  3.4e-05 ***
id4    -0.8552     0.1748   -4.89  1.4e-06 ***
id5     2.0924     0.2012   10.40  < 2e-16 ***
id6     1.1239     0.1680    6.69  6.8e-11 ***
id7     1.4296     0.1706    8.38  7.2e-16 ***
id8    -0.9186     0.1766   -5.20  3.0e-07 ***
id9    -2.4818     0.1667  -14.89  < 2e-16 ***
id10   -0.3469     0.1663   -2.09    0.038 *  
firm2  -0.8446     0.1548   -5.46  8.1e-08 ***
firm3   0.6756     0.1528    4.42  1.2e-05 ***
firm4   0.7481     0.1614    4.64  4.7e-06 ***
firm5  -1.5421     0.1690   -9.12  < 2e-16 ***
firm6   2.1937     0.1587   13.82  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 443 degrees of freedom
  (40 observations deleted due to missingness)
Multiple R-squared:  0.791,	Adjusted R-squared:  0.783 
F-statistic: 98.8 on 17 and 443 DF,  p-value: <2e-16

> 
> proc.time()
   user  system elapsed 
   0.87    0.18    0.98