linearmodels.iv.results.IVGMMResults

class IVGMMResults(results, model)[source]

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