estimation.FixestMulti_.FixestMulti

estimation.FixestMulti_.FixestMulti(
    data,
    copy_data,
    store_data,
    lean,
    fixef_tol,
    fixef_maxiter,
    weights_type,
    use_compression,
    reps,
    seed,
    split,
    fsplit,
    separation_check=None,
    context=0,
    quantreg_method='fn',
    quantreg_multi_method='cfm1',
)

A class to estimate multiple regression models with fixed effects.

Methods

Name Description
coef Obtain the coefficients of the fitted models.
confint Obtain confidence intervals for the fitted models.
fetch_model Fetch a model of class Feols from the Fixest class.
pvalue Obtain the p-values of the fitted models.
se Obtain the standard errors of the fitted models.
tidy Return the results of an estimation using feols() as a tidy Pandas DataFrame.
to_list Return a list of all fitted models.
tstat Obtain the t-statistics of the fitted models.
vcov Update regression inference “on the fly”.
wildboottest Run a wild cluster bootstrap for all regressions in the Fixest object.

coef

estimation.FixestMulti_.FixestMulti.coef()

Obtain the coefficients of the fitted models.

Returns

Name Type Description
pandas.Series A pd.Series with coefficient names and Estimates. The key indicates which models the estimated statistic derives from.

confint

estimation.FixestMulti_.FixestMulti.confint()

Obtain confidence intervals for the fitted models.

Returns

Name Type Description
pandas.Series A pd.Series with coefficient names and confidence intervals. The key indicates which models the estimated statistic derives from.

fetch_model

estimation.FixestMulti_.FixestMulti.fetch_model(i, print_fml=True)

Fetch a model of class Feols from the Fixest class.

Parameters

Name Type Description Default
i int or str The index of the model to fetch. required
print_fml bool Whether to print the formula of the model. Default is True. True

Returns

Name Type Description
A Feols object.

pvalue

estimation.FixestMulti_.FixestMulti.pvalue()

Obtain the p-values of the fitted models.

Returns

Name Type Description
pandas.Series A pd.Series with coefficient names and p-values. The key indicates which models the estimated statistic derives from.

se

estimation.FixestMulti_.FixestMulti.se()

Obtain the standard errors of the fitted models.

Returns

Name Type Description
pandas.Series A pd.Series with coefficient names and standard error estimates. The key indicates which models the estimated statistic derives from.

tidy

estimation.FixestMulti_.FixestMulti.tidy()

Return the results of an estimation using feols() as a tidy Pandas DataFrame.

Returns

Name Type Description
pandas.DataFrame or str A tidy DataFrame with the following columns: - fml: the formula used to generate the results - Coefficient: the names of the coefficients - Estimate: the estimated coefficients - Std. Error: the standard errors of the estimated coefficients - t value: the t-values of the estimated coefficients - Pr(>|t|): the p-values of the estimated coefficients - 2.5%: the lower bound of the 95% confidence interval - 97.5%: the upper bound of the 95% confidence interval If type is set to “markdown”, the resulting DataFrame will be returned as a markdown-formatted string with three decimal places.

to_list

estimation.FixestMulti_.FixestMulti.to_list()

Return a list of all fitted models.

Returns

Name Type Description
A list of all fitted models of types Feols or Fepois.

tstat

estimation.FixestMulti_.FixestMulti.tstat()

Obtain the t-statistics of the fitted models.

Returns

Name Type Description
A pd.Series with coefficient names and estimated t-statistics. The key indicates which models the estimated statistic derives from.

vcov

estimation.FixestMulti_.FixestMulti.vcov(vcov, vcov_kwargs=None)

Update regression inference “on the fly”.

By calling vcov() on a “Fixest” object, all inference procedures applied to the “Fixest” object are replaced with the variance-covariance matrix specified via the method.

Parameters

Name Type Description Default
vcov Union[str, dict[str, str]]) A string or dictionary specifying the type of variance-covariance matrix to use for inference. - If a string, can be one of “iid”, “hetero”, “HC1”, “HC2”, “HC3”. - If a dictionary, it should have the format {“CRV1”: “clustervar”} for CRV1 inference or {“CRV3”: “clustervar”} for CRV3 inference. required
vcov_kwargs Optional[dict[str, any]] Additional keyword arguments for the variance-covariance matrix. None

Returns

Name Type Description
An instance of the "Fixest" class with updated inference.

wildboottest

estimation.FixestMulti_.FixestMulti.wildboottest(
    reps,
    cluster=None,
    param=None,
    weights_type='rademacher',
    impose_null=True,
    bootstrap_type='11',
    seed=None,
    k_adj=True,
    G_adj=True,
)

Run a wild cluster bootstrap for all regressions in the Fixest object.

Parameters

Name Type Description Default
B int The number of bootstrap iterations to run. required
param Union[str, None] A string of length one, containing the test parameter of interest. Default is None. None
cluster Union[str, None] The name of the cluster variable. Default is None. If None, uses the self._clustervar attribute as the cluster variable. If the self._clustervar attribute is None, a heteroskedasticity-robust wild bootstrap is run. None
weights_type str The type of bootstrap weights. Either ‘rademacher’, ‘mammen’, ‘webb’, or ‘normal’. Default is ‘rademacher’. 'rademacher'
impose_null bool Should the null hypothesis be imposed on the bootstrap dgp, or not? Default is True. True
bootstrap_type str A string of length one. Allows choosing the bootstrap type to be run. Either ‘11’, ‘31’, ‘13’, or ‘33’. Default is ‘11’. '11'
seed Union[str, None] Option to provide a random seed. Default is None. None
k_adj bool Whether to adjust the original coefficients with the bootstrap distribution. Default is True. True
G_adj bool Whether to adjust standard errors for clustering in the bootstrap. Default is True. True

Returns

Name Type Description
pandas.DataFrame A pd.DataFrame with bootstrapped t-statistic and p-value. The index indicates which model the estimated statistic derives from.