__init__(*args, **kwds) |
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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(**kwds) |
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f_test(r_matrix[, cov_p, scale, invcov]) |
Compute the F-test for a joint linear hypothesis. |
fittedvalues() |
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get_bse(**kwds) |
standard error of the parameter estimates with options |
initialize(model, params, **kwd) |
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jtest() |
overidentification test |
jval() |
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llf() |
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load(fname) |
load a pickle, (class method) |
normalized_cov_params() |
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predict([exog, transform]) |
Call self.model.predict with self.params as the first argument. |
pvalues() |
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q() |
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remove_data() |
remove data arrays, all nobs arrays from result and model |
resid() |
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save(fname[, remove_data]) |
save a pickle of this instance |
ssr() |
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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 |
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. |