fixest (version 0.3.1)

coeftable: Obtain various statistics from an estimation

Description

Set of functions to directly extract some commonly used statistics, like the p-value or the table of coefficients, from estimations. This was first implemented for fixest estimations, but has some support for other models.

Usage

coeftable(object, se, cluster, ...)
ctable(object, se, cluster, ...)
pvalue(object, se, cluster, ...)
tstat(object, se, cluster, ...)
se(object, se, cluster, ...)

Arguments

object

An estimation. For example obtained from feols.

se

[Fixest specific.] Character scalar. Which kind of standard error should be computed: “standard”, “White”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: se = "cluster", otherwise se = "standard". Note that this argument can be implicitly deduced from the argument cluster.

cluster

[Fixest specific.] Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over var1 and var2 contained in the data.frame base used for the estimation. All the following cluster arguments are valid and do the same thing: cluster = base[, c("var1, "var2")]}, \code{cluster = c("var1, "var2"), cluster = ~var1+var2. If the two variables were used as clusters in the estimation, you could further use cluster = 1:2 or leave it blank with se = "twoway" (assuming var1 [resp. var2] was the 1st [res. 2nd] cluster).

...

Other arguments to be passed to summary.

Value

Returns a table of coefficients, with in rows the variables and four columns: the estimate, the standard-error, the t-statistic and the p-value.

Functions

• pvalue: Extracts the p-value of an estimation

• tstat: Extracts the t-statistics of an estimation

• se: Extracts the standard-error of an estimation

Details

This set of functions is primarily constructed for fixest estimations. Although it can work for non-fixest estimations, support for exotic estimation procedures that do not report standardized coefficient tables is highly limited.

Examples

# NOT RUN {
# Some data and estimation
data(trade)
est = fepois(Euros ~ log(dist_km) | Origin^Product + Year, trade)

#
# Coeftable/se/tstat/pvalue
#

# Default is clustering along Origin^Product
coeftable(est)
se(est)
tstat(est)
pvalue(est)

# Now with two-way clustered standard-errors
#  and using ctable(), the alias to coeftable()

ctable(est, cluster = ~Origin + Product)
se(est, cluster = ~Origin + Product)
pvalue(est, cluster = ~Origin + Product)
tstat(est, cluster = ~Origin + Product)

# Or you can cluster only once:
est_sum = summary(est, cluster = ~Origin + Product)
ctable(est_sum)
se(est_sum)
tstat(est_sum)
pvalue(est_sum)



# }