linearmodels.iv.results.IVGMMResults

class IVGMMResults(results, model)

Results from GMM estimation of IV models

Parameters:

resultsdict[str, any]

A dictionary of results from the model estimation.

model{IVGMM, IVGMMCUE}

The model used to estimate parameters.

Attributes:

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

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