# estimation.FixestMulti_.FixestMulti { #pyfixest.estimation.FixestMulti_.FixestMulti } ```python estimation.FixestMulti_.FixestMulti( data, copy_data, store_data, lean, 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](#pyfixest.estimation.FixestMulti_.FixestMulti.coef) | Obtain the coefficients of the fitted models. | | [confint](#pyfixest.estimation.FixestMulti_.FixestMulti.confint) | Obtain confidence intervals for the fitted models. | | [fetch_model](#pyfixest.estimation.FixestMulti_.FixestMulti.fetch_model) | Fetch a model of class Feols from the Fixest class. | | [pvalue](#pyfixest.estimation.FixestMulti_.FixestMulti.pvalue) | Obtain the p-values of the fitted models. | | [se](#pyfixest.estimation.FixestMulti_.FixestMulti.se) | Obtain the standard errors of the fitted models. | | [tidy](#pyfixest.estimation.FixestMulti_.FixestMulti.tidy) | Return the results of an estimation using `feols()` as a tidy Pandas DataFrame. | | [to_list](#pyfixest.estimation.FixestMulti_.FixestMulti.to_list) | Return a list of all fitted models. | | [tstat](#pyfixest.estimation.FixestMulti_.FixestMulti.tstat) | Obtain the t-statistics of the fitted models. | | [vcov](#pyfixest.estimation.FixestMulti_.FixestMulti.vcov) | Update regression inference "on the fly". | | [wildboottest](#pyfixest.estimation.FixestMulti_.FixestMulti.wildboottest) | Run a wild cluster bootstrap for all regressions in the Fixest object. | ### coef { #pyfixest.estimation.FixestMulti_.FixestMulti.coef } ```python estimation.FixestMulti_.FixestMulti.coef() ``` Obtain the coefficients of the fitted models. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.confint } ```python estimation.FixestMulti_.FixestMulti.confint() ``` Obtain confidence intervals for the fitted models. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.fetch_model } ```python estimation.FixestMulti_.FixestMulti.fetch_model(i, print_fml=True) ``` Fetch a model of class Feols from the Fixest class. #### Parameters {.doc-section .doc-section-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 {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------------|---------------| | | A Feols object. | | ### pvalue { #pyfixest.estimation.FixestMulti_.FixestMulti.pvalue } ```python estimation.FixestMulti_.FixestMulti.pvalue() ``` Obtain the p-values of the fitted models. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.se } ```python estimation.FixestMulti_.FixestMulti.se() ``` Obtain the standard errors of the fitted models. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.tidy } ```python estimation.FixestMulti_.FixestMulti.tidy() ``` Return the results of an estimation using `feols()` as a tidy Pandas DataFrame. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.to_list } ```python estimation.FixestMulti_.FixestMulti.to_list() ``` Return a list of all fitted models. #### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-------------------------------------------------------|---------------| | | A list of all fitted models of types Feols or Fepois. | | ### tstat { #pyfixest.estimation.FixestMulti_.FixestMulti.tstat } ```python estimation.FixestMulti_.FixestMulti.tstat() ``` Obtain the t-statistics of the fitted models. #### Returns {.doc-section .doc-section-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 { #pyfixest.estimation.FixestMulti_.FixestMulti.vcov } ```python 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 {.doc-section .doc-section-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 {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-------------------------------------------------------------|---------------| | | An instance of the \"Fixest\" class with updated inference. | | ### wildboottest { #pyfixest.estimation.FixestMulti_.FixestMulti.wildboottest } ```python 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 {.doc-section .doc-section-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 {.doc-section .doc-section-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. |