linearmodels.iv.results.IVGMMResults¶

class linearmodels.iv.results.IVGMMResults(results: dict[str, Any], model: _IVGMMBase)[source]¶

Results from GMM estimation of IV models

Parameters:¶

results: dict[str, Any]¶

A dictionary of results from the model estimation.

model: _IVGMMBase¶

The model used to estimate parameters.

Methods

c_stat([variables])	C-test of endogeneity
conf_int([level])	Confidence interval construction
predict([exog, endog, data, fitted, ...])	In- and out-of-sample predictions
wald_test([restriction, value, formula])	Test linear equality constraints using a Wald test

Properties

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_model	Model degree of freedom
df_resid	Residual degree of freedom
f_statistic	Model F-statistic
first_stage	First stage regression results
fitted_values	Fitted values
has_constant	Flag indicating the model includes a constant or equivalent
idiosyncratic	Idiosyncratic error
iterations	Iterations used in GMM estimation
j_stat	J-test of overidentifying restrictions
kappa	k-class estimator value
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
weight_config	Weighting matrix configuration used in estimation
weight_matrix	Weight matrix used in the final-step GMM estimation
weight_type	Weighting matrix method used in estimation
wresids	Weighted estimated residuals