statsmodels.sandbox.regression.gmm.IVGMMResults class statsmodels.sandbox.regression.gmm.IVGMMResults(*args, **kwds) [source] Methods 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) f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint lin

statsmodels.tsa.vector_ar.var_model.VARResults.ma_rep VARResults.ma_rep(maxn=10) Compute MA() coefficient matrices Parameters: maxn : int Number of coefficient matrices to compute Returns: coefs : ndarray (maxn x k x k)

statsmodels.sandbox.stats.multicomp.set_remove_subs statsmodels.sandbox.stats.multicomp.set_remove_subs(ssli) [source] remove sets that are subsets of another set from a list of tuples Parameters: ssli : list of tuples each tuple is considered as a set Returns: part : list of tuples new list with subset tuples removed, it is sorted by set-length of tuples. The list contains original tuples, duplicate elements are not removed. Examples >>> set_remove_subs([(0, 1), (1, 2), (1,

statsmodels.tsa.stattools.ccovf statsmodels.tsa.stattools.ccovf(x, y, unbiased=True, demean=True) [source] crosscovariance for 1D Parameters: x, y : arrays time series data unbiased : boolean if True, then denominators is n-k, otherwise n Returns: ccovf : array autocovariance function Notes This uses np.correlate which does full convolution. For very long time series it is recommended to use fft convolution instead.

statsmodels.regression.linear_model.RegressionResults.compare_f_test RegressionResults.compare_f_test(restricted) [source] use F test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. Returns: f_value : float test statistic, F distributed

statsmodels.sandbox.distributions.transformed.SquareFunc.inverseplus SquareFunc.inverseplus(x) [source]

statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes for all pairs something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : array

statsmodels.stats.stattools.omni_normtest statsmodels.stats.stattools.omni_normtest(resids, axis=0) [source] Omnibus test for normality Parameters: resid : array-like axis : int, optional Default is 0 Returns: Chi^2 score, two-tail probability :

statsmodels.duration.hazard_regression.PHRegResults.pvalues static PHRegResults.pvalues()

statsmodels.discrete.discrete_model.LogitResults.save LogitResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove