statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults#

class statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults(model, params, filter_results, cov_type=None, **kwargs)[source]#

Results from fitting a dynamic factor model

Attributes:
aic

(float) Akaike Information Criterion

aicc

(float) Akaike Information Criterion with small sample correction

bic

(float) Bayes Information Criterion

bse

The standard errors of the parameter estimates

coefficients_of_determination

Individual coefficients of determination (\(R^2\))

Coefficients of determination (\(R^2\)) from regressions of endogenous variables on individual estimated factors.

coefficients_of_determinationndarray

A k_endog x k_factors array, where coefficients_of_determination[i, j] represents the \(R^2\) value from a regression of factor j and a constant on endogenous variable i.

get_coefficients_of_determination plot_coefficients_of_determination

cov_params_approx

(array) The variance / covariance matrix. Computed using the numerical Hessian approximated by complex step or finite differences methods.

cov_params_oim

(array) The variance / covariance matrix. Computed using the method from Harvey (1989).

cov_params_opg

(array) The variance / covariance matrix. Computed using the outer product of gradients method.

cov_params_robust

(array) The QMLE variance / covariance matrix. Alias for cov_params_robust_oim

cov_params_robust_approx

(array) The QMLE variance / covariance matrix. Computed using the numerical Hessian as the evaluated hessian.

cov_params_robust_oim

(array) The QMLE variance / covariance matrix. Computed using the method from Harvey (1989) as the evaluated hessian.

factors

Estimates of unobserved factors

fittedvalues

(array) The predicted values of the model. An (nobs x k_endog) array

hqic

(float) Hannan-Quinn Information Criterion

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

loglikelihood_burn

(float) The number of observations during which the likelihood is not evaluated

mae

(float) Mean absolute error

mse

(float) Mean squared error

pvalues

(array) The p-values associated with the z-statistics of the coefficients. Note that the coefficients are assumed to have a Normal distribution

resid

(array) The model residuals. An (nobs x k_endog) array

sse

(float) Sum of squared errors

states
tvalues

Return the t-statistic for a given parameter estimate

use_t

Flag indicating to use the Student’s distribution in inference

zvalues

(array) The z-statistics for the coefficients

Methods

append(endog[, endog_quarterly, refit, ...])

Recreate the results object with new data appended to original data

apply(endog[, k_endog_monthly, ...])

Apply the fitted parameters to new data unrelated to the original data

conf_int([alpha])

Construct confidence interval for the fitted parameters

cov_params([r_matrix, column, scale, cov_p, ...])

Compute the variance/covariance matrix

extend(endog[, endog_quarterly, fit_kwargs, ...])

Recreate the results object for new data that extends original data

f_test(r_matrix[, cov_p, invcov])

Compute the F-test for a joint linear hypothesis

forecast([steps, signal_only])

Out-of-sample forecasts

get_coefficients_of_determination([method, ...])

Get coefficients of determination (R-squared) for variables / factors

get_forecast([steps, signal_only])

Out-of-sample forecasts and prediction intervals

get_prediction([start, end, dynamic, ...])

In-sample prediction and out-of-sample forecasting

get_smoothed_decomposition([...])

Decompose smoothed output into contributions from observations

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

news(comparison[, impact_date, ...])

Compute impacts from updated data (news and revisions)

normalized_cov_params()

See specific model class docstring

plot_coefficients_of_determination([method, ...])

Plot coefficients of determination (R-squared) for variables / factors

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

Diagnostic plots for standardized residuals of one endogenous variable

predict([start, end, dynamic, ...])

In-sample prediction and out-of-sample forecasting

remove_data()

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, use_t])

Compute a t-test for 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[, df_adjust, ...])

Ljung-Box test for no serial correlation of standardized residuals

wald_test(r_matrix[, cov_p, invcov, use_f, ...])

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

Methods

append(endog[, endog_quarterly, refit, ...])

Recreate the results object with new data appended to original data

apply(endog[, k_endog_monthly, ...])

Apply the fitted parameters to new data unrelated to the original data

conf_int([alpha])

Construct confidence interval for the fitted parameters

cov_params([r_matrix, column, scale, cov_p, ...])

Compute the variance/covariance matrix

extend(endog[, endog_quarterly, fit_kwargs, ...])

Recreate the results object for new data that extends original data

f_test(r_matrix[, cov_p, invcov])

Compute the F-test for a joint linear hypothesis

forecast([steps, signal_only])

Out-of-sample forecasts

get_coefficients_of_determination([method, ...])

Get coefficients of determination (R-squared) for variables / factors

get_forecast([steps, signal_only])

Out-of-sample forecasts and prediction intervals

get_prediction([start, end, dynamic, ...])

In-sample prediction and out-of-sample forecasting

get_smoothed_decomposition([...])

Decompose smoothed output into contributions from observations

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

news(comparison[, impact_date, ...])

Compute impacts from updated data (news and revisions)

normalized_cov_params()

See specific model class docstring

plot_coefficients_of_determination([method, ...])

Plot coefficients of determination (R-squared) for variables / factors

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

Diagnostic plots for standardized residuals of one endogenous variable

predict([start, end, dynamic, ...])

In-sample prediction and out-of-sample forecasting

remove_data()

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, use_t])

Compute a t-test for 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[, df_adjust, ...])

Ljung-Box test for no serial correlation of standardized residuals

wald_test(r_matrix[, cov_p, invcov, use_f, ...])

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

aic

(float) Akaike Information Criterion

aicc

(float) Akaike Information Criterion with small sample correction

bic

(float) Bayes Information Criterion

bse

The standard errors of the parameter estimates

coefficients_of_determination

Individual coefficients of determination (\(R^2\))

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.

factors

Estimates of unobserved factors

fittedvalues

(array) The predicted values of the model.

hqic

(float) Hannan-Quinn Information Criterion

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

loglikelihood_burn

(float) The number of observations during which the likelihood is not evaluated

mae

(float) Mean absolute error

mse

(float) Mean squared error

pvalues

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

resid

(array) The model residuals.

sse

(float) Sum of squared errors

states

tvalues

Return the t-statistic for a given parameter estimate

use_t

Flag indicating to use the Student's distribution in inference

zvalues

(array) The z-statistics for the coefficients