broom (version 0.7.2)

tidy.fixest: Tidy a(n) fixest object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for fixest
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A fixest object returned from any of the fixest estimators

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

Additional arguments passed to summary and confint. Important arguments are se and cluster. Other arguments are dof, exact_dof, forceCovariance, and keepBounded. See summary.fixest.

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Details

The fixest package provides a family of functions for estimating models with arbitrary numbers of fixed-effects, in both an OLS and a GLM context. The package also supports robust (i.e. White) and clustered standard error reporting via the generic summary.fixest() command. In a similar vein, the tidy() method for these models allows users to specify a desired standard error correction either 1) implicitly via the supplied fixest object, or 2) explicitly as part of the tidy call. See examples below.

Note that fixest confidence intervals are calculated assuming a normal distribution -- this assumes infinite degrees of freedom for the CI. (This assumption is distinct from the degrees of freedom used to calculate the standard errors. For more on degrees of freedom with clusters and fixed effects, see https://github.com/lrberge/fixest/issues/6 and https://github.com/sgaure/lfe/issues/1#issuecomment-530646990)

See Also

tidy(), fixest::feglm(), fixest::fenegbin(), fixest::feNmlm(), fixest::femlm(), fixest::feols(), fixest::fepois()

Other fixest tidiers: augment.fixest()

Examples

# NOT RUN {
library(fixest)
gravity <- feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product + Year, trade)

tidy(gravity)
glance(gravity)
augment(gravity, trade)

## To get robust or clustered SEs, users can either:
# 1) Or, specify the arguments directly in the tidy() call
tidy(gravity, conf.int = TRUE, cluster = c("Product", "Year"))
tidy(gravity, conf.int = TRUE, se = "threeway")
# 2) Feed tidy() a summary.fixest object that has already accepted these arguments
gravity_summ <- summary(gravity, cluster = c("Product", "Year"))
tidy(gravity_summ, conf.int = TRUE)
# Approach (1) is preferred.

## The other fixest methods all work similarly. For example:
gravity_pois <- feglm(Euros ~ log(dist_km) | Origin + Destination + Product + Year, trade)
tidy(gravity_pois)
glance(gravity_pois)
augment(gravity_pois, trade)
# }
# NOT RUN {
# }

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