linearmodels.iv.absorbing.AbsorbingLSResults¶

class AbsorbingLSResults(results, model)[source]¶

Results from IV estimation

Parameters:

resultsdict[str, any]: A dictionary of results from the model estimation.
modelAbsorbingLS: The model used to estimate parameters.

Attributes:

absorbed_effects: Fitted values from only absorbed terms
absorbed_rsquared: Coefficient of determination (R**2), ignoring absorbed variables
cov: Estimated covariance of parameters
cov_config: Parameter values from covariance estimator
cov_estimator: Type of covariance estimator used to compute covariance
cov_type: Covariance estimator used
debiased: Flag indicating whether covariance uses a small-sample adjustment
df_absorbed: Number of variables absorbed
df_model: Model degree of freedom
df_resid: Residual degree of freedom
f_statistic: Model F-statistic
fitted_values: Fitted values
has_constant: Flag indicating the model includes a constant or equivalent
idiosyncratic: Idiosyncratic error
method: Method used to estimate model parameters
model_ss: Residual sum of squares
nobs: Number of observations
params: Estimated parameters
pvalues: Parameter p-vals.
resid_ss: Residual sum of squares
resids: Estimated residuals
rsquared: Coefficient of determination (R**2)
rsquared_adj: Sample-size adjusted coefficient of determination (R**2)
s2: Residual variance estimator
std_errors: Estimated parameter standard errors
summary: Model estimation summary.
total_ss: Total sum of squares
tstats: Parameter t-statistics
wresids: Weighted estimated residuals

Methods

`conf_int`([level])	Confidence interval construction
`wald_test`([restriction, value, formula])	Test linear equality constraints using a Wald test

Properties

`absorbed_effects`	Fitted values from only absorbed terms
`absorbed_rsquared`	Coefficient of determination (R**2), ignoring absorbed variables
`cov`	Estimated covariance of parameters
`cov_config`	Parameter values from covariance estimator
`cov_estimator`	Type of covariance estimator used to compute covariance
`cov_type`	Covariance estimator used
`debiased`	Flag indicating whether covariance uses a small-sample adjustment
`df_absorbed`	Number of variables absorbed
`df_model`	Model degree of freedom
`df_resid`	Residual degree of freedom
`f_statistic`	Model F-statistic
`fitted_values`	Fitted values
`has_constant`	Flag indicating the model includes a constant or equivalent
`idiosyncratic`	Idiosyncratic error
`method`	Method used to estimate model parameters
`model_ss`	Residual sum of squares
`nobs`	Number of observations
`params`	Estimated parameters
`pvalues`	Parameter p-vals.
`resid_ss`	Residual sum of squares
`resids`	Estimated residuals
`rsquared`	Coefficient of determination (R**2)
`rsquared_adj`	Sample-size adjusted coefficient of determination (R**2)
`s2`	Residual variance estimator
`std_errors`	Estimated parameter standard errors
`summary`	Model estimation summary.
`total_ss`	Total sum of squares
`tstats`	Parameter t-statistics
`wresids`	Weighted estimated residuals