statsmodels.tsa.arima_process.arma_impulse_response statsmodels.tsa.arima_process.arma_impulse_response(ar, ma, nobs=100) [source] get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representati

statsmodels.tsa.arima_process.arma_pacf statsmodels.tsa.arima_process.arma_pacf(ar, ma, nobs=10) [source] partial autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned pacf Returns: pacf : array partial autocorrelation of ARMA process given

statsmodels.tsa.ar_model.AR class statsmodels.tsa.ar_model.AR(endog, dates=None, freq=None, missing='none') [source] Autoregressive AR(p) model Parameters: endog : array-like 1-d endogenous response variable. The independent variable. dates : array-like of datetime, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or ?B?, ?D?, ?W?, ?

statsmodels.regression.linear_model.OLSResults.wald_test OLSResults.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. tuple : A tuple

statsmodels.tsa.ar_model.AR.loglike AR.loglike(params) [source] The loglikelihood of an AR(p) process Parameters: params : array The fitted parameters of the AR model Returns: llf : float The loglikelihood evaluated at params Notes Contains constant term. If the model is fit by OLS then this returns the conditonal maximum likelihood. If it is fit by MLE then the (exact) unconditional maximum likelihood is returned. where is a (p x 1) vector with each element equal to the mean

statsmodels.tsa.ar_model.AR.predict AR.predict(params, start=None, end=None, dynamic=False) [source] Returns in-sample and out-of-sample prediction. Parameters: params : array The fitted model parameters. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first

statsmodels.tsa.ar_model.AR.select_order AR.select_order(maxlag, ic, trend='c', method='mle') [source] Select the lag order according to the information criterion. Parameters: maxlag : int The highest lag length tried. See AR.fit. ic : str {?aic?,?bic?,?hqic?,?t-stat?} Criterion used for selecting the optimal lag length. See AR.fit. trend : str {?c?,?nc?} Whether to include a constant or not. ?c? - include constant. ?nc? - no constant. Returns: bestlag : int Best lag according to I

statsmodels.tsa.vector_ar.dynamic.DynamicVAR class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source] Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares Parameters: data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {?expanding?, ?rolling?} min_periods : int or None Minimum number of observations to require in window, defaults to wi

statsmodels.tsa.vector_ar.var_model.VAR.select_order VAR.select_order(maxlags=None, verbose=True) [source] Compute lag order selections based on each of the available information criteria Parameters: maxlags : int if None, defaults to 12 * (nobs/100.)**(1./4) verbose : bool, default True If True, print table of info criteria and selected orders Returns: selections : dict {info_crit -> selected_order}

statsmodels.tsa.vector_ar.var_model.VARResults.plot_forecast VARResults.plot_forecast(steps, alpha=0.05, plot_stderr=True) [source] Plot forecast