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.

statsmodels.discrete.discrete_model.NegativeBinomialResults.pvalues static NegativeBinomialResults.pvalues()

statsmodels.robust.robust_linear_model.RLM.initialize RLM.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.sandbox.regression.gmm.GMM.fitgmm GMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) [source] estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin