Estimates OLS with any number of fixed-effects.
feols(
fml,
data,
weights,
offset,
panel.id,
fixef,
fixef.tol = 1e-06,
fixef.iter = 10000,
collin.tol = 1e-10,
nthreads = getFixest_nthreads(),
verbose = 0,
warn = TRUE,
notes = getFixest_notes(),
combine.quick,
demeaned = FALSE,
mem.clean = FALSE,
only.env = FALSE,
env,
...
)A formula representing the relation to be estimated. For example: fml = z~x+y. To include fixed-effects, insert them in this formula using a pipe: e.g. fml = z~x+y | fe_1+fe_2. You can combine two fixed-effects with ^: e.g. fml = z~x+y|fe_1^fe_2, see details. You can also use variables with varying slopes using square brackets: e.g. in fml = z~y|fe_1[x] + fe_2 the variable x will have one coefficient for each value of fe_1 -- if you use varying slopes, please have a look at the details section (can't describe it all here).
A data.frame containing the necessary variables to run the model. The variables of the non-linear right hand side of the formula are identified with this data.frame names. Can also be a matrix.
A formula or a numeric vector. Each observation can be weighted, the weights must be greater than 0. If equal to a formula, it should be of one-sided: for example ~ var_weight.
A formula or a numeric vector. An offset can be added to the estimation. If equal to a formula, it should be of the form (for example) ~0.5*x**2. This offset is linearly added to the elements of the main formula 'fml'.
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).
Character vector. The names of variables to be used as fixed-effects. These variables should contain the identifier of each observation (e.g., think of it as a panel identifier). Note that the recommended way to include fixed-effects is to insert them directly in the formula.
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.
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000.
Numeric scalar, default is 1e-14. Threshold decising 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).
The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. The default is to use 50% of all threads. You can set permanently the number of threads used within this package using the function setFixest_nthreads.
Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algoritmh (the first number is the left-hand-side, the other numbers are the right-hand-side variables).
Logical, default is TRUE. Whether warnings should be displayed (concerns warnings relating to convergence state).
Logical. By default, two notes are displayed: when NAs are removed (to show additional information) and when some observations are removed because of collinearity. To avoid displaying these messages, you can set notes = FALSE. You can remove these messages permanently by using setFixest_notes(FALSE).
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.
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.
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.
(Advanced users.) Logical, default is FALSE. If TRUE, then only the environment used to make the estimation is returned.
(Advanced users.) A fixest environment created by a fixest estimation with only.env = TRUE. Default is missing. If provided, the data from this environment will be used to perform the estimation.
Not currently used.
A fixest object.
The number of observations.
The linear formula of the call.
The call of the function.
The method used to estimate the model.
The family used to estimate the model.
(When relevant.) The "full" formula containing the linear part and the fixed-effects.
The number of parameters of the model.
The names of each fixed-effect dimension.
The list (of length the number of fixed-effects) of the fixed-effects identifiers for each observation.
The size of each fixed-effect (i.e. the number of unique identifierfor each fixed-effect dimension).
The named vector of estimated coefficients.
Logical, if multicollinearity was found.
The table of the coefficients with their standard errors, z-values and p-values.
The loglikelihood.
Sum of the squared residuals of the null model (containing only with the intercept).
Sum of the squared residuals of the model estimated with fixed-effects only.
The log-likelihood of the null model (containing only with the intercept).
The log-likelihood of the model estimated with fixed-effects only.
The adjusted pseudo R2.
The fitted values.
The linear predictors.
The residuals (y minus the fitted values).
Squared correlation between the dependent variable and the expected predictor (i.e. fitted.values) obtained by the estimation.
The Hessian of the parameters.
The variance-covariance matrix of the parameters.
The standard-error of the parameters.
The matrix of the scores (first derivative for each observation).
The difference between the dependent variable and the expected predictor.
The sum of the fixed-effects coefficients for each observation.
(When relevant.) The offset formula.
(When relevant.) The weights formula.
(When relevant.) Vector of observations that were removed because of NA values.
(When relevant.) Vector containing the variables removed because of collinearity.
(When relevant.) Vector of coefficients, where the values of the variables removed because of collinearity are NA.
The minimal diagonal value of the Cholesky decomposition. Small values indicate possible presence collinearity.
Only when demeaned = TRUE: the centered dependent variable.
Only when demeaned = TRUE: the centered explanatory variable.
You can combine two variables to make it a new fixed-effect using ^. The syntax is as follows: fe_1^fe_2. Here you created a new variable which is the combination of the two variables fe_1 and fe_2. This is identical to doing paste0(fe_1, "_", fe_2) but more convenient.
Note that pasting is a costly operation, especially for large data sets. Thus, the internal algorithm uses a numerical trick which is fast, but the drawback is that the identity of each observation is lost (i.e. they are now equal to a meaningless number instead of being equal to paste0(fe_1, "_", fe_2)). These “identities” are useful only if you're interested in the value of the fixed-effects (that you can extract with fixef.fixest). If you're only interested in coefficients of the variables, it doesn't matter. Anyway, you can use combine.quick = FALSE to tell the internal algorithm to use paste instead of the numerical trick. By default, the numerical trick is performed only for large data sets.
You can add variables with varying slopes in the fixed-effect part of the formula. The syntax is as follows: fixef_var[var1, var2]. Here the variables var1 and var2 will be with varying slopes (one slope per value in fixef_var) and the fixed-effect fixef_var will also be added.
To add only the variables with varying slopes and not the fixed-effect, use double square brackets: fixef_var[[var1, var2]].
In other words:
fixef_var[var1, var2] is equivalent to fixef_var + fixef_var[[var1]] + fixef_var[[var2]]
fixef_var[[var1, var2]] is equivalent to fixef_var[[var1]] + fixef_var[[var2]]
To use leads/lags of variables in the estimation, you can: i) either provide the argument panel.id, ii) either set your data set as a panel with the function panel. Doing either of the two will give you acceess to the lagging functions l and f.
You can provide several leads/lags at once: e.g. if your formula is equal to f(y) ~ l(x, -1:1), it means that the dependent variable is equal to the lead of y, and you will have as explanatory variables the lead of x1, x1 and the lag of x1. See the examples in function l for more details.
You can interact a numeric variable with a "factor-like" variable by using interact(var, fe, ref), where fe is the variable to be interacted with and the argument ref is a value of fe taken as a reference (optional). Instead of using the function interact, you can use the alias i(var, fe, ref) or even the highly specific syntax var::fe(ref).
It is important to note that *if you do not care about the standard-errors of the interactions*, then you can add interactions in the fixed-effects part of the formula (using the syntax fe[[var]], as explained in the section “Varying slopes”).
Using this specific way to create interactions leads to a different display of the interacted values in etable and offers a special representation of the interacted coefficients in the function coefplot. See examples.
The function interact has in fact more arguments, please see details in its associated help page.
Standard-errors can be computed in different ways, you can use the arguments se and dof in summary.fixest to define how to compute them. By default, in the presence of fixed-effects, standard-errors are automatically clustered.
The following vignette: On standard-errors describes in details how the standard-errors are computed in fixest and how you can replicate standard-errors from other software.
You can use the functions setFixest_se and setFixest_dof to permanently set the way the standard-errors are computed.
The method used to demean each variable along the fixed-effects is based on Berge (2018), since this is the same problem to solve as for the Gaussian case in a ML setup.
Berge, Laurent, 2018, "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm." CREA Discussion Papers, 13 (https://wwwen.uni.lu/content/download/110162/1299525/file/2018_13).
For models with multiple fixed-effects:
Gaure, Simen, 2013, "OLS with multiple high dimensional category variables", Computational Statistics & Data Analysis 66 pp. 8--18
See also summary.fixest to see the results with the appropriate standard-errors, fixef.fixest to extract the fixed-effects coefficients, and the function etable to visualize the results of multiple estimations. For plotting coefficients: see coefplot.
And other estimation methods: femlm, feglm, fepois, fenegbin, feNmlm.
# NOT RUN {
#
# Just one set of fixed-effects:
#
res = feols(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris)
summary(res)
#
# Varying slopes:
#
res = feols(Sepal.Length ~ Petal.Length | Species[Sepal.Width], iris)
summary(res)
#
# Combining the FEs:
#
base = iris
base$fe_2 = rep(1:10, 15)
res_comb = feols(Sepal.Length ~ Petal.Length | Species^fe_2, base)
summary(res_comb)
fixef(res_comb)[[1]]
#
# Using leads/lags:
#
data(base_did)
# We need to set up the panel with the arg. panel.id
est1 = feols(y~l(x1, 0:1), base_did, panel.id = ~id+period)
est2 = feols(f(y)~l(x1, -1:1), base_did, panel.id = ~id+period)
etable(est1, est2, order = "f", drop="Int")
#
# Using interactions:
#
# NOTA: in fixest estimations, i(var, fe, ref) is equivalent to var::fe(ref)
data(base_did)
# We interact the variable 'period' with the variable 'treat'
est_did = feols(y ~ x1 + i(treat, period, 5) | id+period, base_did)
# You could have used the following formula instead:
# y ~ x1 + treat::period(5) | id+period
# Now we can plot the result of the interaction with coefplot
coefplot(est_did)
# You have many more example in coefplot help
# }
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