statsmodels.tsa.statespace.mlemodel.MLEResults¶
-
class
statsmodels.tsa.statespace.mlemodel.
MLEResults
(model, params, results, cov_type=None, cov_kwds=None, **kwargs)[source]¶ Class to hold results from fitting a state space model.
- Parameters
See also
- Attributes
- 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.
- model
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, fit_kwargs])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, 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
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
(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
(float) Mean squared error
(array) The p-values associated with the z-statistics of the coefficients.
(array) The model residuals.
(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.