statsmodels.tsa.ardl.UECMResults¶
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class statsmodels.tsa.ardl.UECMResults(model, params, cov_params, normalized_cov_params=
None, scale=1.0, use_t=False)[source]¶ Class to hold results from fitting an UECM model.
- Parameters:¶
- model
UECM Reference to the model that is fit.
- params
ndarray The fitted parameters from the AR Model.
- cov_params
ndarray The estimated covariance matrix of the model parameters.
- normalized_cov_params
ndarray The array inv(dot(x.T,x)) where x contains the regressors in the model.
- scale
float,optional An estimate of the scale of the model.
- model
- Attributes:¶
- aic
Akaike Information Criterion using Lutkepohl’s definition.
\(-2 llf + \ln(nobs) (1 + df_{model})\)
- aicc
Akaike Information Criterion with small sample correction
\(2.0 * df_{model} * nobs / (nobs - df_{model} - 1.0)\)
ar_lagsThe autoregressive lags included in the model
- arfreq
Returns the frequency of the AR roots.
This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.
- bic
Bayes Information Criterion
\(-2 llf + \ln(nobs) (1 + df_{model})\)
- bse
The standard errors of the estimated parameters.
If method is ‘cmle’, then the standard errors that are returned are the OLS standard errors of the coefficients. If the method is ‘mle’ then they are computed using the numerical Hessian.
- ci_bse
Standard Errors of normalized cointegrating relationship
- ci_params
Parameters of normalized cointegrating relationship
- ci_pvalues
P-values of normalized cointegrating relationship
- ci_resids
- ci_tvalues
T-values of normalized cointegrating relationship
df_modelThe degrees of freedom consumed by the model.
df_residThe remaining degrees of freedom in the residuals.
- fittedvalues
The in-sample predicted values of the fitted AR model.
The k_ar initial values are computed via the Kalman Filter if the model is fit by mle.
- fpe
Final prediction error using Lütkepohl’s definition.
\(((nobs+df_{model})/(nobs-df_{model})) \sigma^2\)
- hqic
Hannan-Quinn Information Criterion using Lutkepohl’s definition.
\(-2 llf + 2 \ln(\ln(nobs)) (1 + df_{model})\)
- llf
Log-likelihood of model
nobsThe number of observations after adjusting for losses due to lags.
paramsThe estimated parameters.
- pvalues
The two-tailed p values for the t-stats of the params.
- resid
The residuals of the model.
- roots
The roots of the AR process.
The roots are the solution to (1 - arparams[0]*z - arparams[1]*z**2 -…- arparams[p-1]*z**k_ar) = 0. Stability requires that the roots in modulus lie outside the unit circle.
- tvalues
Return the t-statistic for a given parameter estimate.
use_tFlag indicating to use the Student’s distribution in inference.
Methods
append(endog[, exog, refit, fit_kwargs])Append observations to the ones used to fit the model
apply(endog[, exog, refit, fit_kwargs])Apply the fitted parameters to new data unrelated to the original data
bounds_test(case[, cov_type, cov_kwds, ...])Cointegration bounds test of Pesaran, Shin, and Smith
ci_conf_int([alpha])Covariance of normalized of cointegrating relationship
ci_summary([alpha])conf_int([alpha, cols])Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])Compute the variance/covariance matrix.
Returns a summary containing standard model diagnostic tests
f_test(r_matrix[, cov_p, invcov])Compute the F-test for a joint linear hypothesis.
forecast([steps, exog, fixed])Out-of-sample forecasts
get_prediction([start, end, dynamic, exog, ...])Predictions and prediction intervals
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([lags, fig, figsize])Diagnostic plots for standardized residuals
plot_predict([start, end, dynamic, exog, ...])Plot in- and out-of-sample predictions
predict([start, end, dynamic, exog, ...])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.
scale()sigma2()summary([alpha])Summarize the Model
t_test(r_matrix[, cov_p, 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([lags])ARCH-LM test of residual heteroskedasticity
Test for normality of standardized residuals.
test_serial_correlation([lags, model_df])Ljung-Box test for residual serial correlation
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
Akaike Information Criterion using Lutkepohl's definition.
Akaike Information Criterion with small sample correction
The autoregressive lags included in the model
Returns the frequency of the AR roots.
Bayes Information Criterion
The standard errors of the estimated parameters.
Standard Errors of normalized cointegrating relationship
Parameters of normalized cointegrating relationship
P-values of normalized cointegrating relationship
T-values of normalized cointegrating relationship
The degrees of freedom consumed by the model.
The remaining degrees of freedom in the residuals.
The in-sample predicted values of the fitted AR model.
Final prediction error using Lütkepohl's definition.
Hannan-Quinn Information Criterion using Lutkepohl's definition.
Log-likelihood of model
The number of observations after adjusting for losses due to lags.
The estimated parameters.
The two-tailed p values for the t-stats of the params.
The residuals of the model.
The roots of the AR process.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student's distribution in inference.