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. |
fittedvalues() | |
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 |
resid() | |
save(fname[, remove_data]) | save a pickle of this instance |
ssr() | |
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 |