R Under development (unstable) (2024-02-24 r85984 ucrt) -- "Unsuffered Consequences"
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> library(lfe)
Loading required package: Matrix
> options(digits=3,warn=1,lfe.threads=2)
> set.seed(42)
> x <- rnorm(100)
> x2 <- rnorm(length(x))
> x3 <- rnorm(length(x))
> id <- factor(sample(10, length(x), replace=TRUE))
> id.eff <- rnorm(nlevels(id))
> firm <- factor(sample(10,length(x),replace=TRUE))
> 
> y <- x + x2 + x3 + id.eff[id] + rnorm(length(x))
> z <- x/2 + x2/2 + x3/2 + id.eff[id]/2 + rnorm(length(x))
> v <- x/3 + x2/2 + x3/2 + id.eff[id]/2 + rnorm(length(x))
> w <- x/2 + x2/3 + x3/2 + id.eff[id]/3 + rnorm(length(x))
> 
> est <- felm(v|w|y|z ~ x+x2+x3|id,keepX=TRUE)
> for(lh in est$lhs) {
+     print(summary(est, lhs=lh, robust=TRUE))
+ }
Summary for outcome v 

Call:
   felm(formula = v | w | y | z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4995 -0.4254 -0.0321  0.4885  1.7591 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x    0.5637     0.0739    7.63  2.8e-11 ***
x2   0.4178     0.0858    4.87  4.9e-06 ***
x3   0.5643     0.0846    6.67  2.3e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.843 on 87 degrees of freedom
Multiple R-squared(full model): 0.638   Adjusted R-squared: 0.588 
Multiple R-squared(proj model): 0.526   Adjusted R-squared: 0.461 
F-statistic(full model, *iid*):12.8 on 12 and 87 DF, p-value: 1.34e-14 
F-statistic(proj model): 50.7 on 3 and 87 DF, p-value: <2e-16 


Summary for outcome w 

Call:
   felm(formula = v | w | y | z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2058 -0.5929 -0.0948  0.6021  2.6465 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x     0.681      0.102    6.67  2.2e-09 ***
x2    0.450      0.131    3.43  0.00092 ***
x3    0.451      0.089    5.06  2.3e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.924 on 87 degrees of freedom
Multiple R-squared(full model): 0.587   Adjusted R-squared: 0.53 
Multiple R-squared(proj model): 0.506   Adjusted R-squared: 0.437 
F-statistic(full model, *iid*):10.3 on 12 and 87 DF, p-value: 2.75e-12 
F-statistic(proj model): 22.5 on 3 and 87 DF, p-value: 6.99e-11 


Summary for outcome y 

Call:
   felm(formula = v | w | y | z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3645 -0.6425  0.0859  0.6470  2.9411 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x     1.151      0.088   13.09  < 2e-16 ***
x2    1.000      0.120    8.32  1.1e-12 ***
x3    1.173      0.108   10.89  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 87 degrees of freedom
Multiple R-squared(full model): 0.833   Adjusted R-squared: 0.81 
Multiple R-squared(proj model): 0.772   Adjusted R-squared: 0.741 
F-statistic(full model, *iid*):36.1 on 12 and 87 DF, p-value: <2e-16 
F-statistic(proj model): 94.9 on 3 and 87 DF, p-value: <2e-16 


Summary for outcome z 

Call:
   felm(formula = v | w | y | z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6990 -0.6658  0.0189  0.6380  1.9743 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x    0.4902     0.0879    5.57  2.7e-07 ***
x2   0.6401     0.1138    5.62  2.2e-07 ***
x3   0.4422     0.1147    3.86  0.00022 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.02 on 87 degrees of freedom
Multiple R-squared(full model): 0.599   Adjusted R-squared: 0.544 
Multiple R-squared(proj model): 0.441   Adjusted R-squared: 0.364 
F-statistic(full model, *iid*):10.8 on 12 and 87 DF, p-value: 8.33e-13 
F-statistic(proj model): 28.6 on 3 and 87 DF, p-value: 5.6e-13 


> summary(est, lhs='z', robust=TRUE)
Summary for outcome z 

Call:
   felm(formula = v | w | y | z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6990 -0.6658  0.0189  0.6380  1.9743 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x    0.4902     0.0879    5.57  2.7e-07 ***
x2   0.6401     0.1138    5.62  2.2e-07 ***
x3   0.4422     0.1147    3.86  0.00022 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.02 on 87 degrees of freedom
Multiple R-squared(full model): 0.599   Adjusted R-squared: 0.544 
Multiple R-squared(proj model): 0.441   Adjusted R-squared: 0.364 
F-statistic(full model, *iid*):10.8 on 12 and 87 DF, p-value: 8.33e-13 
F-statistic(proj model): 28.6 on 3 and 87 DF, p-value: 5.6e-13 


> summary(zest <- felm(z ~x+x2+x3|id,keepX=TRUE), robust=TRUE)

Call:
   felm(formula = z ~ x + x2 + x3 | id, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6990 -0.6658  0.0189  0.6380  1.9743 

Coefficients:
   Estimate Robust s.e t value Pr(>|t|)    
x    0.4902     0.0879    5.57  2.7e-07 ***
x2   0.6401     0.1138    5.62  2.2e-07 ***
x3   0.4422     0.1147    3.86  0.00022 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.02 on 87 degrees of freedom
Multiple R-squared(full model): 0.599   Adjusted R-squared: 0.544 
Multiple R-squared(proj model): 0.441   Adjusted R-squared: 0.364 
F-statistic(full model, *iid*):10.8 on 12 and 87 DF, p-value: 8.33e-13 
F-statistic(proj model): 28.6 on 3 and 87 DF, p-value: 5.6e-13 


> getfe(zest,se=TRUE)
       effect obs comp fe idx    se
id.1   0.0713   5    1 id   1 0.460
id.2  -0.3484   8    1 id   2 0.373
id.3  -0.7928  13    1 id   3 0.291
id.4  -0.5728   7    1 id   4 0.397
id.5  -1.6214  10    1 id   5 0.328
id.6   1.2285   7    1 id   6 0.387
id.7   0.7963  14    1 id   7 0.279
id.8   0.5192  15    1 id   8 0.265
id.9  -1.0537   5    1 id   9 0.460
id.10 -0.4860  16    1 id  10 0.260
> getfe(est,method='cg',se=TRUE,lhs=c('w','z'))
      effect.w effect.z obs comp fe idx  se.w  se.z
id.1     0.277   0.0713   5    1 id   1 0.416 0.460
id.2    -0.193  -0.3484   8    1 id   2 0.337 0.373
id.3    -0.555  -0.7928  13    1 id   3 0.263 0.291
id.4    -0.407  -0.5728   7    1 id   4 0.359 0.397
id.5    -1.145  -1.6214  10    1 id   5 0.296 0.328
id.6    -0.475   1.2285   7    1 id   6 0.350 0.387
id.7     1.011   0.7963  14    1 id   7 0.252 0.279
id.8     0.326   0.5192  15    1 id   8 0.240 0.265
id.9    -0.993  -1.0537   5    1 id   9 0.416 0.460
id.10   -0.665  -0.4860  16    1 id  10 0.236 0.260
> lhs <- cbind(v,w,y,z)
> rhs <- cbind(x,x2,x3)
> est <- felm(lhs ~rhs|id+firm,keepX=TRUE)
> summary(est,lhs='lhs.z', robust=TRUE)
Summary for outcome lhs.z 

Call:
   felm(formula = lhs ~ rhs | id + firm, keepX = TRUE) 

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2337 -0.6746  0.0996  0.6950  1.8839 

Coefficients:
      Estimate Robust s.e t value Pr(>|t|)    
rhsx     0.521      0.101    5.16  1.8e-06 ***
rhsx2    0.639      0.129    4.96  4.1e-06 ***
rhsx3    0.429      0.126    3.40   0.0011 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 78 degrees of freedom
Multiple R-squared(full model): 0.633   Adjusted R-squared: 0.534 
Multiple R-squared(proj model): 0.449   Adjusted R-squared: 0.301 
F-statistic(full model, *iid*): 6.4 on 21 and 78 DF, p-value: 6.04e-10 
F-statistic(proj model): 20.5 on 3 and 78 DF, p-value: 6.98e-10 


> getfe(est,se=TRUE)
        effect.lhs.v effect.lhs.w effect.lhs.y effect.lhs.z obs comp   fe idx
id.1          0.8749       0.9812       0.0489       0.7051   5    1   id   1
id.2          0.6719       0.4853       0.8638       0.0607   8    1   id   2
id.3          0.3024       0.1536      -0.4686      -0.4533  13    1   id   3
id.4          0.0844       0.2636      -0.0926      -0.0300   7    1   id   4
id.5         -0.3246      -0.2747      -0.7442      -1.2239  10    1   id   5
id.6          2.1050       0.1077       2.1595       1.5731   7    1   id   6
id.7          1.3908       1.8396       3.0891       1.2949  14    1   id   7
id.8          1.0757       0.9470       1.0288       1.0310  15    1   id   8
id.9         -0.0124       0.0539      -0.1982      -0.4388   5    1   id   9
id.10         0.0000       0.0000       0.0000       0.0000  16    1   id  10
firm.1       -0.8998      -0.4904      -0.7812      -0.7076  12    1 firm   1
firm.2       -1.0174      -1.0420      -1.4526      -0.1498  14    1 firm   2
firm.3       -1.2929      -0.5287      -1.0098      -0.4653  12    1 firm   3
firm.4       -0.8283      -0.7822      -0.4942      -0.3447  12    1 firm   4
firm.5       -1.2683      -1.3940      -1.0347      -0.8927   8    1 firm   5
firm.6       -2.0526      -0.2337      -0.6150       0.2459   3    1 firm   6
firm.7       -0.9234      -1.0914      -1.7224      -0.8636   6    1 firm   7
firm.8       -1.1723      -0.5550      -1.2184      -0.3741  13    1 firm   8
firm.9       -0.8319      -0.0858      -0.8495      -0.1335   9    1 firm   9
firm.10      -0.6497      -0.8919      -1.0865      -0.7919  11    1 firm  10
        se.lhs.v se.lhs.w se.lhs.y se.lhs.z
id.1       0.419    0.358    0.484    0.500
id.2       0.328    0.340    0.449    0.429
id.3       0.297    0.305    0.397    0.353
id.4       0.410    0.416    0.459    0.413
id.5       0.328    0.334    0.431    0.350
id.6       0.365    0.378    0.472    0.477
id.7       0.292    0.270    0.380    0.353
id.8       0.277    0.280    0.319    0.328
id.9       0.418    0.433    0.557    0.555
id.10      0.000    0.000    0.000    0.000
firm.1     0.233    0.252    0.316    0.336
firm.2     0.300    0.287    0.382    0.326
firm.3     0.284    0.281    0.327    0.361
firm.4     0.321    0.288    0.360    0.383
firm.5     0.384    0.315    0.496    0.436
firm.6     0.548    0.466    0.654    0.680
firm.7     0.394    0.457    0.495    0.478
firm.8     0.279    0.279    0.370    0.382
firm.9     0.340    0.333    0.388    0.420
firm.10    0.277    0.291    0.333    0.388
> 
> proc.time()
   user  system elapsed 
   2.40    0.21    2.60