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

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

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

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

Vector Autoregressions tsa.vector_ar VAR(p) processes We are interested in modeling a multivariate time series , where denotes the number of observations and the number of variables. One way of estimating relationships between the time series and their lagged values is the vector autoregression process: where is a coefficient matrix. We follow in large part the methods and notation of Lutkepohl (2005), which we will not develop here. Model fitting Note The classes referenced below ar

statsmodels.sandbox.distributions.transformed.ExpTransf_gen class statsmodels.sandbox.distributions.transformed.ExpTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_

statsmodels.regression.linear_model.GLS.information GLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.