statsmodels.tsa.statespace.sarimax.SARIMAXResults

class statsmodels.tsa.statespace.sarimax.SARIMAXResults(model, params, filter_results, cov_type='opg', **kwargs)[source]

Class to hold results from fitting an SARIMAX model.

Parameters:

model : SARIMAX instance

The fitted model instance

Attributes

specification dictionary Dictionary including all attributes from the SARIMAX model instance.
polynomial_ar array Array containing autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_ma array Array containing moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_seasonal_ar array Array containing seasonal autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_seasonal_ma array Array containing seasonal moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_trend array Array containing trend polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
model_orders list of int The orders of each of the polynomials in the model.
param_terms list of str List of parameters actually included in the model, in sorted order.

Methods

aic() (float) Akaike Information Criterion
arfreq() (array) Frequency of the roots of the reduced form autoregressive
arparams() (array) Autoregressive parameters actually estimated in the model.
arroots() (array) Roots of the reduced form autoregressive lag polynomial
bic() (float) Bayes Information Criterion
bse()
conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...]) Returns the variance/covariance matrix.
cov_params_cs() (array) The variance / covariance matrix. Computed using the numerical
cov_params_delta() (array) The variance / covariance matrix. Computed using the numerical
cov_params_oim() (array) The variance / covariance matrix. Computed using the method
cov_params_opg() (array) The variance / covariance matrix. Computed using the outer
cov_params_robust() (array) The QMLE variance / covariance matrix. Alias for
cov_params_robust_cs() (array) The QMLE variance / covariance matrix. Computed using the
cov_params_robust_oim() (array) The QMLE variance / covariance matrix. Computed using the
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
fittedvalues() (array) The predicted values of the model.
forecast([steps, exog]) Out-of-sample forecasts
hqic() (float) Hannan-Quinn Information Criterion
initialize(model, params, **kwd)
llf() (float) The value of the log-likelihood function evaluated at params.
llf_obs() (float) The value of the log-likelihood function evaluated at params.
load(fname) load a pickle, (class method)
loglikelihood_burn() (float) The number of observations during which the likelihood is not
mafreq() (array) Frequency of the roots of the reduced form moving average
maparams() (array) Moving average parameters actually estimated in the model.
maroots() (array) Roots of the reduced form moving average lag polynomial
normalized_cov_params()
predict([start, end, exog, dynamic]) In-sample prediction and out-of-sample forecasting
pvalues() (array) The p-values associated with the z-statistics of the
remove_data() remove data arrays, all nobs arrays from result and model
resid() (array) The model residuals.
save(fname[, remove_data]) save a pickle of this instance
summary([alpha, start]) Summarize the Model
t_test(r_matrix[, cov_p, scale, use_t]) Compute a t-test for a each linear hypothesis of the form Rb = q
tvalues() Return the t-statistic for a given parameter estimate.
wald_test(r_matrix[, cov_p, scale, invcov, ...]) Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, ...]) Compute a sequence of Wald tests for terms over multiple columns
zvalues() (array) The z-statistics for the coefficients.

Attributes

use_t