statsmodels.tsa.ar_model.ARResults.pvalues static ARResults.pvalues() [source]

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

statsmodels.sandbox.distributions.transformed.loggammaexpg statsmodels.sandbox.distributions.transformed.loggammaexpg = univariate distribution of a non-linear monotonic transformation of a random variable

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit_loc_scale NormExpan_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

statsmodels.genmod.generalized_estimating_equations.GEEResults.wald_test GEEResults.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.

statsmodels.tsa.ar_model.ARResults.t_test ARResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 the for

Autoregressive Moving Average (ARMA): Artificial data Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) Generate some data from an ARMA process: In [2]: arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) The conventions of the arma_generate function require that we specify a 1 f

statsmodels.tsa.arima_model.ARIMAResults class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Methods aic() arfreq() Returns the frequency of the AR roots. arparams() arroots() bic() bse() conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params() f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. fittedvalues() forecast([steps, exog,

statsmodels.tsa.ar_model.AR.initialize AR.initialize() [source]