statsmodels.tsa.statespace.sarimax.SARIMAXResults¶
-
class
statsmodels.tsa.statespace.sarimax.
SARIMAXResults
(model, params, filter_results, cov_type=None, **kwargs)[source]¶ Class to hold results from fitting an SARIMAX model.
- Parameters
- model
SARIMAX
instance
The fitted model instance
- model
See also
- 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.
- specification
Methods
append
(endog[, exog, refit, fit_kwargs])Recreate the results object with new data appended to the original data
apply
(endog[, exog, refit, fit_kwargs])Apply the fitted parameters to new data unrelated to the original data
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters.
cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix.
extend
(endog[, exog])Recreate the results object for new data that extends the original data
f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
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
impulse_responses
([steps, impulse, …])Impulse response function
info_criteria
(criteria[, method])Information criteria
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
See specific model class docstring
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
Remove data arrays, all nobs arrays from result and model.
save
(fname[, remove_data])Save a pickle of this instance.
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
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.
Properties
(float) Akaike Information Criterion
(float) Akaike Information Criterion with small sample correction
(array) Frequency of the roots of the reduced form autoregressive lag polynomial
(array) Autoregressive parameters actually estimated in the model.
(array) Roots of the reduced form autoregressive lag polynomial
(float) Bayes Information Criterion
The standard errors of the parameter estimates.
(array) The variance / covariance matrix.
(array) The variance / covariance matrix.
(array) The variance / covariance matrix.
(array) The QMLE variance / covariance matrix.
(array) The QMLE variance / covariance matrix.
(array) The QMLE variance / covariance matrix.
(array) The predicted values of the model.
(float) Hannan-Quinn Information Criterion
(float) The value of the log-likelihood function evaluated at params.
(float) The value of the log-likelihood function evaluated at params.
(float) The number of observations during which the likelihood is not evaluated.
(float) Mean absolute error
(array) Frequency of the roots of the reduced form moving average lag polynomial
(array) Moving average parameters actually estimated in the model.
(array) Roots of the reduced form moving average lag polynomial
(float) Mean squared error
(array) The p-values associated with the z-statistics of the coefficients.
(array) The model residuals.
(array) Seasonal autoregressive parameters actually estimated in the model.
(array) Seasonal moving average parameters actually estimated in the model.
(float) Sum of squared errors
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.
(array) The z-statistics for the coefficients.