estimation.fepois_.Fepois

estimation.fepois_.Fepois(
    self
    FixestFormula
    data
    ssc_dict
    drop_singletons
    drop_intercept
    weights
    weights_type
    collin_tol
    fixef_tol
    lookup_demeaned_data
    tol
    maxiter
    solver='np.linalg.solve'
    store_data=True
    copy_data=True
    lean=False
    sample_split_var=None
    sample_split_value=None
    separation_check=None
)

Estimate a Poisson regression model.

Non user-facing class to estimate a Poisson regression model via Iterated Weighted Least Squares (IWLS).

Inherits from the Feols class. Users should not directly instantiate this class, but rather use the fepois() function. Note that no demeaning is performed in this class: demeaning is performed in the FixestMulti class (to allow for caching of demeaned variables for multiple estimation).

The method implements the algorithm from Stata’s ppmlhdfe module.

Attributes

Name	Type	Description
_Y	np.ndarray	The demeaned dependent variable, a two-dimensional numpy array.
_X	np.ndarray	The demeaned independent variables, a two-dimensional numpy array.
_fe	np.ndarray	Fixed effects, a two-dimensional numpy array or None.
weights	np.ndarray	Weights, a one-dimensional numpy array or None.
coefnames	list[str]	Names of the coefficients in the design matrix X.
drop_singletons	bool	Whether to drop singleton fixed effects.
collin_tol	float	Tolerance level for the detection of collinearity.
maxiter	Optional[int], default=25	Maximum number of iterations for the IRLS algorithm.
tol	Optional[float], default=1e-08	Tolerance level for the convergence of the IRLS algorithm.
solver	(str, default is np.linalg.solve)	Solver to use for the estimation. Alternative is ‘np.linalg.lstsq’.
fixef_tol	float, default = 1e-08.	Tolerance level for the convergence of the demeaning algorithm.
solver
weights_name	Optional[str]	Name of the weights variable.
weights_type	Optional[str]	Type of weights variable.
_data	pd.DataFrame	The data frame used in the estimation. None if arguments `lean = True` or `store_data = False`.

Methods

Name	Description
get_fit	Fit a Poisson Regression Model via Iterated Weighted Least Squares (IWLS).
predict	Return predicted values from regression model.
prepare_model_matrix	Prepare model inputs for estimation.
resid	Return residuals from regression model.
to_array	Turn estimation DataFrames to np arrays.

get_fit

estimation.fepois_.Fepois.get_fit()

Fit a Poisson Regression Model via Iterated Weighted Least Squares (IWLS).

Returns

Name	Type	Description
	None

Attributes

Name	Type	Description
beta_hat	np.ndarray	Estimated coefficients.
Y_hat	np.ndarray	Estimated dependent variable.
u_hat	np.ndarray	Estimated residuals.
weights	np.ndarray	Weights (from the last iteration of the IRLS algorithm).
X	np.ndarray	Demeaned independent variables (from the last iteration of the IRLS algorithm).
Z	np.ndarray	Demeaned independent variables (from the last iteration of the IRLS algorithm).
Y	np.ndarray	Demeaned dependent variable (from the last iteration of the IRLS algorithm).

predict

estimation.fepois_.Fepois.predict(
    newdata=None
    atol=1e-06
    btol=1e-06
    type='link'
)

Return predicted values from regression model.

Return a flat np.array with predicted values of the regression model. If new fixed effect levels are introduced in newdata, predicted values for such observations will be set to NaN.

Parameters

Name	Type	Description	Default
newdata	Union[None, pd.DataFrame]	A pd.DataFrame with the new data, to be used for prediction. If None (default), uses the data used for fitting the model.	`None`
atol	Float	Stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html	`1e-6`
btol	Float	Another stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html	`1e-6`
type	str	The type of prediction to be computed. Can be either “response” (default) or “link”. If type=“response”, the output is at the level of the response variable, i.e., it is the expected predictor E(Y\|X). If “link”, the output is at the level of the explanatory variables, i.e., the linear predictor X @ beta.	`'link'`
atol	Float	Stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lsqr.html	`1e-6`
btol	Float	Another stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lsqr.html	`1e-6`

Returns

Name	Type	Description
	np.ndarray	A flat array with the predicted values of the regression model.

prepare_model_matrix

estimation.fepois_.Fepois.prepare_model_matrix()

Prepare model inputs for estimation.

resid

estimation.fepois_.Fepois.resid(type='response')

Return residuals from regression model.

Parameters

Name	Type	Description	Default
type	str	The type of residuals to be computed. Can be either “response” (default) or “working”.	`'response'`

Returns

Name	Type	Description
	np.ndarray	A flat array with the residuals of the regression model.

to_array

estimation.fepois_.Fepois.to_array()

Turn estimation DataFrames to np arrays.