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

specification

Dictionary including all attributes from the SARIMAX model instance.

Type

dictionary

polynomial_ar

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).

Type

array

polynomial_ma

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).

Type

array

polynomial_seasonal_ar

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).

Type

array

polynomial_seasonal_ma

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).

Type

array

polynomial_trend

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).

Type

array

model_orders

The orders of each of the polynomials in the model.

Type

list of int

param_terms

List of parameters actually included in the model, in sorted order.

Type

list of str

Methods

aic()

(float) Akaike Information Criterion

arfreq()

(array) Frequency of the roots of the reduced form autoregressive lag polynomial

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_approx()

(array) The variance / covariance matrix.

cov_params_oim()

(array) The variance / covariance matrix.

cov_params_opg()

(array) The variance / covariance matrix.

cov_params_robust()

(array) The QMLE variance / covariance matrix.

cov_params_robust_approx()

(array) The QMLE variance / covariance matrix.

cov_params_robust_oim()

(array) The QMLE variance / covariance matrix.

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])

Out-of-sample forecasts

get_forecast([steps])

Out-of-sample forecasts

get_prediction([start, end, dynamic, index, …])

In-sample prediction and out-of-sample forecasting

hqic()

(float) Hannan-Quinn Information Criterion

impulse_responses([steps, impulse, …])

Impulse response function

info_criteria(criteria[, method])

Information criteria

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 evaluated.

mafreq()

(array) Frequency of the roots of the reduced form moving average lag polynomial

maparams()

(array) Moving average parameters actually estimated in the model.

maroots()

(array) Roots of the reduced form moving average lag polynomial

normalized_cov_params()

plot_diagnostics([variable, lags, fig, figsize])

Diagnostic plots for standardized residuals of one endogenous variable

predict([start, end, dynamic])

In-sample prediction and out-of-sample forecasting

pvalues()

(array) The p-values associated with the z-statistics of the coefficients.

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

seasonalarparams()

(array) Seasonal autoregressive parameters actually estimated in the model.

seasonalmaparams()

(array) Seasonal moving average parameters actually estimated in the model.

simulate(nsimulations[, measurement_shocks, …])

Simulate a new time series following the state space model

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

t_test_pairwise(term_name[, method, alpha, …])

perform pairwise t_test with multiple testing corrected p-values

test_heteroskedasticity(method[, …])

Test for heteroskedasticity of standardized residuals

test_normality(method)

Test for normality of standardized residuals.

test_serial_correlation(method[, lags])

Ljung-box test for no serial correlation of standardized residuals

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