fixest (version 0.8.4)

setFixest_estimation: Default arguments for fixest estimations

Description

This function sets globally the default arguments of fixest estimations.

Usage

setFixest_estimation(
  fixef.rm = "perfect",
  fixef.tol = 1e-06,
  fixef.iter = 10000,
  collin.tol = 1e-10,
  lean = FALSE,
  verbose = 0,
  warn = TRUE,
  combine.quick = NULL,
  demeaned = FALSE,
  mem.clean = FALSE,
  glm.iter = 25,
  glm.tol = 1e-08,
  panel.id = NULL,
  reset = FALSE
)
getFixest_estimation()

Arguments

fixef.rm

Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. The meaning of "both" and "none" is direct.

fixef.tol

Precision used to obtain the fixed-effects. Defaults to 1e-5. It corresponds to the maximum absolute difference allowed between two coefficients of successive iterations. Argument fixef.tol cannot be lower than 10000*.Machine$double.eps. Note that this parameter is dynamically controlled by the algorithm.

fixef.iter

Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000.

collin.tol

Numeric scalar, default is 1e-10. Threshold deciding when variables should be considered collinear and subsequently removed from the estimation. Higher values means more variables will be removed (if there is presence of collinearity). One signal of presence of collinearity is t-stats that are extremely low (for instance when t-stats < 1e-3).

lean

Logical, default is FALSE. If TRUE then all large objects are removed from the returned result: this will save memory but will block the possibility to use many methods. It is recommended to use the arguments se or cluster to obtain the appropriate standard-errors at estimation time, since obtaining different SEs won't be possible afterwards.

verbose

Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algorithm (the first number is the left-hand-side, the other numbers are the right-hand-side variables).

warn

Logical, default is TRUE. Whether warnings should be displayed (concerns warnings relating to convergence state).

combine.quick

Logical. When you combine different variables to transform them into a single fixed-effects you can do e.g. y ~ x | paste(var1, var2). The algorithm provides a shorthand to do the same operation: y ~ x | var1^var2. Because pasting variables is a costly operation, the internal algorithm may use a numerical trick to hasten the process. The cost of doing so is that you lose the labels. If you are interested in getting the value of the fixed-effects coefficients after the estimation, you should use combine.quick = FALSE. By default it is equal to FALSE if the number of observations is lower than 50,000, and to TRUE otherwise.

demeaned

Logical, default is FALSE. Only used in the presence of fixed-effects: should the centered variables be returned? If TRUE, it creates the items y_demeaned and X_demeaned.

mem.clean

Logical, default is FALSE. Only to be used if the data set is large compared to the available RAM. If TRUE then intermediary objects are removed as much as possible and gc is run before each substantial C++ section in the internal code to avoid memory issues.

glm.iter

Number of iterations of the glm algorithm. Default is 25.

glm.tol

Tolerance level for the glm algorithm. Default is 1e-8.

panel.id

The panel identifiers. Can either be: i) a one sided formula (e.g. panel.id = ~id+time), ii) a character vector of length 2 (e.g. panel.id=c('id', 'time'), or iii) a character scalar of two variables separated by a comma (e.g. panel.id='id,time'). Note that you can combine variables with ^ only inside formulas (see the dedicated section in feols).

reset

Logical, default to FALSE. Whether to reset all values.

Value

The function getFixest_estimation returns the currently set global defaults.

Examples

# NOT RUN {
#
# Example: removing singletons is FALSE by default
#

# => changing this default

# Let's create data with singletons
base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
base$fe_singletons = as.character(base$species)
base$fe_singletons[1:5] = letters[1:5]

res          = feols(y ~ x1 + x2 | fe_singletons, base)
res_noSingle = feols(y ~ x1 + x2 | fe_singletons, base, fixef.rm = "single")

# New defaults
setFixest_estimation(fixef.rm = "single")
res_newDefault = feols(y ~ x1 + x2 | fe_singletons, base)

etable(res, res_noSingle, res_newDefault)

# Resetting the defaults
setFixest_estimation(reset = TRUE)



# }