The table below shows the correspondance between regression models in Stata and R
| Stata | R |
|---|---|
| y x1 x2 | y ~ x1 + x2 |
| y x1,nocons | y ~ 0 + x1 |
| y i.x1 | y ~ as.factor(x1) |
| y c.x1#c.x2 | y ~ x1:x2 |
| y c.x1##c.x2 | y ~ x1*x2 |
| y c.x1##i.x2 | y ~ x1*as.factor(x2) |
The package lfe implements models with high dimensional fixed effects or/and instrumental variables
N <- 1e6
df <- tibble(
id1 = sample(c("id01", "id02", "id03"), N, TRUE),
id2 = sample(5, N, TRUE),
y = sample(round(runif(100, max = 100), 4), N, TRUE),
x1 = sample(round(runif(100, max = 100), 4), N, TRUE),
x2 = sample(round(runif(100, max = 100), 4), N, TRUE),
x3 = sample(round(runif(100, max = 100), 4), N, TRUE)
)
You first need to convert categorical variables into factors:
df <- df %>% mutate(id1 = as.factor(id1))
df <- df %>% mutate(id2 = as.factor(id2))
To estimate a linear model:
| Stata | areg y x1 [w=x3], a(id1) cl(id1) |
| lfe | felm(y ~ x1 | id1 | 0 | id1, df, weight = x3)) |
| Stata | reghdfe y x3 (x2 = x1), a(id1) cl(id1 id2) |
| lfe | felm(y ~ x3 | id1 | (x2 ~ x1) | id1 + id2, df) |
| Stata | reghdfe y x2, a(c.x3#i.id1 id1) cl(id1 id2) |
| lfe | felm(y ~ x2 | x3:id1 + id1, df) |
Errors reported by felm are similar to the ones given by areg and not xtivreg/xtivreg2. Manual adjustments can be done similarly to Gormley and Matsa.
gmm implements GMMrdd implements regression discontinuity models.matchit implements matching procedures.An estimation function returns a list that contains the estimates, the covariance matrix, and in a lot of cases, the residuals, the predicted values, or the original variables used in the estimation. Apply the names function to examine the result:
result <- felm(y ~ x2, df)
names(result)
#> [1] "coefficients" "badconv" "Pp" "N" "p"
#> [6] "inv" "beta" "response" "fitted.values" "residuals"
#> [11] "r.residuals" "terms" "cfactor" "numrefs" "df"
#> [16] "df.residual" "rank" "exactDOF" "vcv" "robustvcv"
#> [21] "clustervcv" "cse" "ctval" "cpval" "clustervar"
#> [26] "se" "tval" "pval" "rse" "rtval"
#> [31] "rpval" "xp" "call"
pryr::object_size(result)
#> [1] 88 MB
Applying summary prints a table similar to Stata output
summary(result)
#> Call:
#> felm(formula = y ~ x2, data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -48.834 -23.175 -5.028 25.222 50.939
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 48.746112 0.064228 758.949 <2e-16 ***
#> x2 0.001997 0.001059 1.886 0.0593 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 29.91 on 999998 degrees of freedom
#> Multiple R-squared: 3.556e-06 Adjusted R-squared: 1.556e-06
#> F-statistic:3.556 on 1 and 999998 DF, p-value: 0.05934
The package stargazer allows to combine several regression results in a table:
stargazer(result, type = "text")
#> ===============================================
#> Dependent variable:
#> ---------------------------
#> y
#> -----------------------------------------------
#> x2 -0.0004
#> (0.001)
#>
#> Constant 50.315***
#> (0.064)
#>
#> -----------------------------------------------
#> Observations 1,000,000
#> R2 0.00000
#> Adjusted R2 -0.00000
#> Residual Std. Error 29.707 (df = 999998)
#> ===============================================
#> Note: *p<0.1; **p<0.05; ***p<0.01