class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds)
[source]
just a storage class right now
bse () | |
calc_cov_params (moms, gradmoms[, weights, …]) | calculate covariance of parameter estimates |
compare_j (other) | overidentification test for comparing two nested gmm estimates |
conf_int ([alpha, cols, method]) | Returns the confidence interval of the fitted parameters. |
cov_params ([r_matrix, column, scale, cov_p, …]) | Returns the variance/covariance matrix. |
f_test (r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
get_bse (**kwds) | standard error of the parameter estimates with options |
initialize (model, params, **kwd) | |
jtest () | overidentification test |
jval () | |
llf () | |
load (fname) | load a pickle, (class method) |
normalized_cov_params () | |
predict ([exog, transform]) | Call self.model.predict with self.params as the first argument. |
pvalues () | |
q () | |
remove_data () | remove data arrays, all nobs arrays from result and model |
save (fname[, remove_data]) | save a pickle of this instance |
summary ([yname, xname, title, alpha]) | Summarize the Regression Results |
t_test (r_matrix[, cov_p, scale, use_t]) | Compute a t-test for a each linear hypothesis of the form Rb = q |
t_test_pairwise (term_name[, method, alpha, …]) | perform pairwise t_test with multiple testing corrected p-values |
tvalues () | Return the t-statistic for a given parameter estimate. |
wald_test (r_matrix[, cov_p, scale, invcov, …]) | Compute a Wald-test for a joint linear hypothesis. |
wald_test_terms ([skip_single, …]) | Compute a sequence of Wald tests for terms over multiple columns |
bse_ | standard error of the parameter estimates |
use_t |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.sandbox.regression.gmm.GMMResults.html