Time Series Filters Link to Notebook GitHub In [1]: from __future__ import print_function import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm In [2]: dta = sm.datasets.macrodata.load_pandas().data In [3]: index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')) print(index) <class 'pandas.tseries.index.DatetimeIndex'> [1959-03-31, ..., 2009-09-30] Length: 203, Freq: None, Timezone: None In [4]:

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

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

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.sandbox.regression.gmm.IVRegressionResults.get_robustcov_results IVRegressionResults.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.robust.robust_linear_model.RLMResults.bcov_scaled static RLMResults.bcov_scaled() [source]

statsmodels.tools.tools.monotone_fn_inverter statsmodels.tools.tools.monotone_fn_inverter(fn, x, vectorized=True, **keywords) Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.

statsmodels.sandbox.regression.gmm.IVGMMResults.tvalues static IVGMMResults.tvalues() Return the t-statistic for a given parameter estimate.