statsmodels.tsa.arima_model.ARIMA.geterrors ARIMA.geterrors(params) Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond_mean NonlinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.tsa.arima_model.ARIMAResults.mafreq static ARIMAResults.mafreq() Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.sandbox.tsa.fftarma.ArmaFft class statsmodels.sandbox.tsa.fftarma.ArmaFft(ar, ma, n) [source] fft tools for arma processes This class contains several methods that are providing the same or similar returns to try out and test different implementations. Notes TODO: check whether we don?t want to fix maxlags, and create new instance if maxlag changes. usage for different lengths of timeseries ? or fix frequency and length for fft check default frequencies w, terminology norw n_or_w

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

statsmodels.regression.quantile_regression.QuantRegResults.get_robustcov_results QuantRegResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC3 static IVRegressionResults.cov_HC3() See statsmodels.RegressionResults

Getting started This very simple case-study is designed to get you up-and-running quickly with statsmodels. Starting from raw data, we will show the steps needed to estimate a statistical model and to draw a diagnostic plot. We will only use functions provided by statsmodels or its pandas and patsy dependencies. Loading modules and functions After installing statsmodels and its dependencies, we load a few modules and functions: In [1]: import statsmodels.api as sm In [2]: import pandas In [3

statsmodels.sandbox.regression.gmm.GMM.score GMM.score(params, weights, epsilon=None, centered=True) [source]

statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.