felm {capybara}R Documentation

LM fitting with high-dimensional k-way fixed effects

Description

feglm can be used to fit linear models with many high-dimensional fixed effects. The estimation procedure is based on unconditional maximum likelihood and can be interpreted as a “weighted demeaning” approach.

Usage

felm(formula = NULL, data = NULL, weights = NULL, vcov = NULL, control = NULL)

Arguments

formula an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.
data an object of class "data.frame" containing the variables in the model. The expected input is a dataset with the variables specified in formula and a number of rows at least equal to the number of variables in the model.
weights an optional string with the name of the prior weights variable in data.
vcov an optional character string specifying the type of variance-covariance estimator. One of "iid" (default OLS, ignore cluster part of formula), "hetero" (heteroskedastic-robust HC0, computed in C++ - no cluster variable needed), "cluster" (one-way sandwich using the cluster variable in the formula), "m-estimator" (M-estimator one-way sandwich), or "dyadic" (Cameron-Miller dyadic sandwich; requires two entity variables in the third part of the formula). When NULL (default), the type is inferred from the formula: if a cluster variable is present the standard sandwich is used, otherwise the inverse Hessian (IID) is returned.
control a named list of parameters for controlling the fitting process. See fit_control for details.

Value

A named list of class "felm". The list contains the following eleven elements:

coefficients a named vector of the estimated coefficients
fitted_values a vector of the estimated dependent variable
weights a vector of the weights used in the estimation
hessian a matrix with the numerical second derivatives
null_deviance the null deviance of the model
nobs a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations
fe_levels a named vector with the number of levels in each fixed effect
nms_fe a list with the names of the fixed effects variables
formula the formula used in the model
data the data used in the model after dropping non-contributing observations
control the control list used in the model

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model with fixed effects
fit <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
summary(fit)

ross2004_subset <- ross2004[ross2004$year %in% c(1994, 1999), ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model without fixed effects but with clustered standard errors
# Note: Use 0 to indicate no fixed effects when specifying clusters
fit <- felm(ltrade ~ ldist | 0 | year, ross2004_subset)
summary(fit)

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