statsmodels.tsa.statespace.mlemodel.MLEResults

class statsmodels.tsa.statespace.mlemodel.MLEResults(model, params, results, cov_type='opg', cov_kwds=None, **kwargs)[source]

Class to hold results from fitting a state space model.

Parameters:
  • model (MLEModel instance) – The fitted model instance
  • params (array) – Fitted parameters
  • filter_results (KalmanFilter instance) – The underlying state space model and Kalman filter output
model

Model instance – A reference to the model that was fit.

filter_results

KalmanFilter instance – The underlying state space model and Kalman filter output

nobs

float – The number of observations used to fit the model.

params

array – The parameters of the model.

scale

float – This is currently set to 1.0 unless the model uses concentrated filtering.

Methods

aic() (float) Akaike Information Criterion
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.
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
simulate(nsimulations[, measurement_shocks, …]) Simulate a new time series following the state space model
summary([alpha, start, title, model_name, …]) 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