fixest (version 0.1.2)

esttable: Facility to display the results of multiple fixest estimations.

Description

This function aggregates the results of multiple estimations and display them in the form of only one table whose row names are the variables and the columns contain the coefficients and standard-errors.

Usage

esttable(..., se = c("standard", "white", "cluster", "twoway",
  "threeway", "fourway"), dof = TRUE, cluster, depvar, drop, order,
  digits = 4, pseudo = TRUE, convergence, signifCode = c(`***` =
  0.001, `**` = 0.01, `*` = 0.05, . = 0.1), titles, keepFactors = FALSE,
  family, bic = TRUE, loglik = FALSE)

Arguments

...

Used to capture different fixest objects (obtained with femlm, feols or feglm). Note that any other type of element is discarded. Note that you can give a list of fixest objects.

se

Character scalar. Which kind of standard error should be computed: “standard”, “White”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: se = "cluster", otherwise se = "standard". Note that this argument can be implicitly deduced from the argument cluster.

dof

Logical, default is TRUE. Should there be a degree of freedom correction to the standard errors of the coefficients?

cluster

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over var1 and var2 contained in the data.frame base used for the estimation. All the following cluster arguments are valid and do the same thing: cluster = base[, c("var1, "var2")]}, \code{cluster = c("var1, "var2"), cluster = ~var1+var2. If the two variables were used as clusters in the estimation, you could further use cluster = 1:2 or leave it blank with se = "twoway" (assuming var1 [resp. var2] was the 1st [res. 2nd] cluster).

depvar

Logical, default is missing. Whether a first line containing the dependent variables should be shown. By default, the dependent variables are shown only if they differ across models.

drop

Character vector. This element is used if some variables are not to be displayed. This should be a regular expression (see regex help for more info). There can be more than one regular expression. Each variable satisfying the regular expression will be discarded.

order

Character vector. This element is used if the user wants the variables to be ordered in a certain way. This should be a regular expression (see regex help for more info). There can be more than one regular expression. The variables satisfying the first regular expression will be placed first, then the order follows the sequence of regular expressions.

digits

Integer, default is 4. The number of digits to be displayed.

pseudo

Logical, default is TRUE. Should the pseudo R2 be displayed?

convergence

Logical, default is missing. Should the convergence state of the algorithm be displayed? By default, convergence information is displayed if at least one model did not converge.

signifCode

Named numeric vector, used to provide the significance codes with respect to the p-value of the coefficients. Default is c("***"=0.001, "**"=0.01, "*"=0.05, "."=0.10).

titles

A character vector. The length must match the number of models.

keepFactors

Logical, default is TRUE. If FALSE, then factor variables are displayed as fixed-effects and no coefficient is shown.

family

A logical, default is missing. Whether to display the families of the models. By default this line is displayed when at least two models are from different families.

bic

Logical, default is TRUE.Should the BIC be reported?

loglik

Logical, default is TRUE. Should the log-likelihood be reported?

Value

Returns a data.frame containing the formatted results.

See Also

See also the main estimation functions femlm, feols or feglm. Use summary.fixest to see the results with the appropriate standard-errors, fixef.fixest to extract the cluster coefficients, and the functions esttable and esttex to visualize the results of multiple estimations.

Examples

# NOT RUN {
# two fitted models with different expl. variables:
res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
            Petal.Width | Species, iris)
# estimation without clusters
res2 = update(res1, . ~ Sepal.Width | 0)

# We export the two results in one Latex table:
esttable(res1, res2)

# With clustered standard-errors + showing the dependent variable
esttable(res1, res2, se = "cluster", cluster = iris$Species, depvar = TRUE)

# Changing the model names + the order of the variables
# + dropping the intercept.
esttable(model_1 = res1, res2,
          order = c("Width", "Petal"), drop = "Int",
          signifCode = c("**" = 0, "*" = 0.2, "n.s."=1))



# }