summclust.fixest {summclust}

Compute Influence and Leverage Metrics for objects of type fixest

Description

Compute influence and leverage metrics for clustered inference based on the Cluster Jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type fixest.

Usage

## S3 method for class 'fixest'
summclust(
  obj,
  cluster,
  params,
  absorb_cluster_fixef = TRUE,
  type = "CRV3",
  ...
)

Arguments

obj

An object of type fixest

cluster

A clustering vector

params

A character vector of variables for which leverage statistics should be computed. If NULL, leverage statistics will be computed for all k model covariates

absorb_cluster_fixef

TRUE by default. Should the cluster fixed effects be projected out? This increases numerical stability and decreases computational costs

type

"CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb

...

other function arguments passed to 'vcov'

Value

An object of type summclust, including a CRV3 variance-covariance estimate as described in MacKinnon, Nielsen & Webb (2022)

coef_estimates

The coefficient estimates of the linear model.

vcov

A CRV3 or CRV3J variance-covariance matrix estimate as described in MacKinnon, Nielsen & Webb (2022)

leverage_g

A vector of leverages.

leverage_avg

The cluster leverage.

partial_leverage

The partial leverages.

coef_var_leverage_avg

Coefficient of Variation for the leverage statistic

coef_var_leverage_g

Coefficient of Variation for the Partial Leverage Statistics

coef_var_N_G

Coefficient of Variation for the Cluster Sizes.

beta_jack

The jackknifed' leave-on-cluster-out regression coefficients.

params

The input parameter vector 'params'.

N_G

The number of clusters-

call

The summclust() function call.

cluster

The names of the clusters.

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples

library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)