statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC1 static IVRegressionResults.cov_HC1() See statsmodels.RegressionResults

statsmodels.regression.linear_model.RegressionResults.compare_lr_test RegressionResults.compare_lr_test(restricted, large_sample=False) [source] Likelihood ratio 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. large_sample : bool Flag

statsmodels.miscmodels.count.PoissonOffsetGMLE.information PoissonOffsetGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.distributions.transformed.Transf_gen.nnlf Transf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.sandbox.regression.gmm.GMM class statsmodels.sandbox.regression.gmm.GMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] Class for estimation by Generalized Method of Moments needs to be subclassed, where the subclass defined the moment conditions momcond Parameters: endog : array endogenous variable, see notes exog : array array of exogenous variables, see notes instrument : array array of instruments, see notes nmoms : None or int nu

statsmodels.sandbox.stats.multicomp.GroupsStats class statsmodels.sandbox.stats.multicomp.GroupsStats(x, useranks=False, uni=None, intlab=None) [source] statistics by groups (another version) groupstats as a class with lazy evaluation (not yet - decorators are still missing) written this time as equivalent of scipy.stats.rankdata gs = GroupsStats(X, useranks=True) assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15) TODO: incomplete doc strings Methods groupdemean() groupss

statsmodels.discrete.discrete_model.LogitResults.pvalues static LogitResults.pvalues()

Time Series analysis tsa statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It

statsmodels.discrete.discrete_model.BinaryModel.pdf BinaryModel.pdf(X) The probability density (mass) function of the model.

statsmodels.tsa.ar_model.ARResults.wald_test ARResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays in