statsmodels.sandbox.regression.gmm.IVGMMResults.normalized_cov_params IVGMMResults.normalized_cov_params()

statsmodels.graphics.gofplots.ProbPlot.theoretical_percentiles static ProbPlot.theoretical_percentiles() [source]

statsmodels.regression.linear_model.WLS class statsmodels.regression.linear_model.WLS(endog, exog, weights=1.0, missing='none', hasconst=None, **kwargs) [source] A regression model with diagonal but non-identity covariance structure. The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W. Parameters: endog : array-like 1-d endogenous response variable. The d

statsmodels.regression.linear_model.OLSResults.uncentered_tss static OLSResults.uncentered_tss()

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.predict LinearIVGMM.predict(params, exog=None) [source]

statsmodels.regression.linear_model.OLSResults.rsquared_adj static OLSResults.rsquared_adj()

statsmodels.tsa.vector_ar.var_model.VAR.information VAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.gmm.IVGMMResults.q static IVGMMResults.q()

statsmodels.tsa.x13.x13_arima_analysis statsmodels.tsa.x13.x13_arima_analysis(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, retspec=False, speconly=False, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True) [source] Perform x13-arima analysis for monthly or quarterly data. Parameters: endog : array-like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex