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

statsmodels.discrete.discrete_model.Poisson class statsmodels.discrete.discrete_model.Poisson(endog, exog, offset=None, exposure=None, missing='none', **kwargs) [source] Poisson model for count data Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tool

statsmodels.genmod.families.links.identity.deriv2 identity.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, opti

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

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

statsmodels.genmod.families.links.nbinom.inverse nbinom.inverse(z) Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.tsa.ar_model.AR.fit AR.fit(maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver='lbfgs', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the unconditional maximum likelihood of an AR(p) process. Parameters: maxlag : int If ic is None, then maxlag is the lag length used in fit. If ic is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(no

statsmodels.sandbox.distributions.extras.ACSkewT_gen.est_loc_scale ACSkewT_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.discrete.discrete_model.Logit.information Logit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.