Unprecise p-values in Stargazer

Question

I want the same stars for significancies in regression output in stargazer as in the "normal output".

I produce data

library("stargazer"); library("lmtest"); library("sandwich")
set.seed(1234)
df <- data.frame(y=1001:1100)
df$x <- c(1:70,-100:-71) + rnorm(100, 0, 74.8)
model <- lm(log(y) ~ x, data=df)

and get some model estimates where the coefficient on x has a p-value of 0.1023

coeftest(model, vcov = vcovHC(model, type="HC3"))

I want to have these results in LaTeX. Based on the same function I calculate heteroscedasticity consistent standard estimates and let stargazer use them.

stderr_HC3_model <- sqrt(diag(vcovHC(model, type = "HC3")))
stargazer(model, se=list(stderr_HC3_model))

The stargazer output has a star at the coefficient indicating significance when alpha=10%. I want stargazer to give the same as the coeftest. (Because of the comparability with Stata where reg L_y x, vce(hc3) gives exactly the coeftest results.)

I played around with the stargazer options p.auto, t.auto which did not help. When I execute "stargazer" I cannot view the underlying code as it is possible in other cases. What to do?

---

Richards answer helped me. I indicate the steps I used to give out more than one regression (let's say ols_a and ols_b).

ses <- list(coeftest(ols_a, vcov = vcovHC(ols_a, type="HC3"))[,2],
        coeftest(ols_b, vcov = vcovHC(ols_b, type="HC3"))[,2])
pvals <- list(coeftest(ols_a, vcov = vcovHC(ols_a, type="HC3"))[,4],
          coeftest(ols_b, vcov = vcovHC(ols_b, type="HC3"))[,4])
stargazer(ols_a, ols_b, type="text", p=pvals, se=ses)

Answer 1

There are inherent dangers associated with the se argument.

When using this approach, the user should be cautious wrt the arguments t.auto and p.auto, both of which default to TRUE. I think it would be cautious to set them both to FALSE, and supply manually t and p values.

Failure to do so, and you risk getting significance stars not in sync with the displayed p-values. (I suspect that stargazer will simply reuse the se, which are now different from the default ones, and recompute the displayed stars using this input; which will naturally yield unexpected results.)

See also:

• Displaying p-values instead of SEs in parenthesis

Answer 2

You need to provide the p values associated with your coeftest. From the man page.

p a list of numeric vectors that will replace the default p-values for each model. Matched by element names. These will form the basis of decisions about significance stars

The following should work.

test <- coeftest(model, vcov = vcovHC(model, type="HC3"))
ses <- test[, 2]
pvals <- test[, 4]
stargazer(model, type="text", p=pvals, se=ses)

This provides the following.

===============================================
                        Dependent variable:    
                    ---------------------------
                              log(y)           
-----------------------------------------------
x                            -0.00005          


Constant                     6.956***          
                              (0.003)          

-----------------------------------------------
Observations                    100            
R2                             0.026           
Adjusted R2                    0.016           
Residual Std. Error       0.027 (df = 98)      
F Statistic             2.620 (df = 1; 98)     
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

Answer 3

It may be a minor issue but Richard's answer is actually not entirely correct - his stargazer output does not report any standard errors nor potential significance stars for the variable x.

Also when reporting only a single model in stargazer manual coefficients, se, p and t values have to be provided in a list. Otherwise stargazer will report an empty list.

The (slightly) corrected example:

test <- coeftest(model, vcov = vcovHC(model, type="HC3"))
ses <- list(test[, 2])
pvals <- list(test[, 4])
stargazer(model, type="text", p=pvals, se=ses)

Output:

=======================================================================
                                         Dependent variable:           
                              -----------------------------------------
                                        Daily added investors          
                                              negative                 
                                              binomial                 
-----------------------------------------------------------------------
log(lag_raised_amount + 1)                    -0.466***                
                                               (0.124)                 

lag_target1                                   -0.661***                
                                               (0.134)                 

Constant                                      -3.480**                 
                                               (1.290)                 

-----------------------------------------------------------------------
Observations                                    6,513                  
Log Likelihood                                 -8,834                
theta                                     1.840*** (0.081)             
Akaike Inf. Crit.                              17,924                
=======================================================================
Note:                         + p<0.1; * p<0.05; ** p<0.01; *** p<0.001