statsmodels.regression.linear_model.OLSResults.normalized_cov_params OLSResults.normalized_cov_params()

statsmodels.discrete.discrete_model.LogitResults.normalized_cov_params LogitResults.normalized_cov_params()

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.r2 static DynamicVAR.r2() [source] Returns the r-squared values.

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.T static DynamicVAR.T() [source] Number of time periods in results

statsmodels.tsa.arima_model.ARMAResults class statsmodels.tsa.arima_model.ARMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an ARMA model. Parameters: model : ARMA instance The fitted model instance params : array Fitted parameters normalized_cov_params : array, optional The normalized variance covariance matrix scale : float, optional Optional argument to scale the variance covariance matrix. Returns: **Attributes** : ai

statsmodels.tsa.arima_model.ARIMAResults.tvalues static ARIMAResults.tvalues() Return the t-statistic for a given parameter estimate.

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

statsmodels.sandbox.tsa.fftarma.ArmaFft.periodogram ArmaFft.periodogram(nobs=None) periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencie

statsmodels.sandbox.tsa.fftarma.ArmaFft.spddirect ArmaFft.spddirect(n) [source] power spectral density using padding to length n done by fft currently returns two-sided according to fft frequencies, use first half

statsmodels.sandbox.tsa.fftarma.ArmaFft.generate_sample ArmaFft.generate_sample(nsample=100, scale=1.0, distrvs=None, axis=0, burnin=0) generate ARMA samples Parameters: nsample : int or tuple of ints If nsample is an integer, then this creates a 1d timeseries of length size. If nsample is a tuple, then the timeseries is along axis. All other axis have independent arma samples. scale : float standard deviation of noise distrvs : function, random number generator function that generates