| aa_gmvarkit-package | gmvarkit: Estimate Gaussian Mixture Vector Autoregressive (GMVAR) model |
| aa_gmvarkit | gmvarkit: Estimate Gaussian Mixture Vector Autoregressive (GMVAR) model |
| add_data | Add data to object of class 'gmvar' defining a GMVAR model |
| all_pos_ints | Check whether all arguments are positive scalar whole numbers |
| calc_gradient | Calculate gradient or Hessian matrix |
| calc_hessian | Calculate gradient or Hessian matrix |
| change_parametrization | Change parametrization of the parameter vector |
| change_regime | Change regime parameters *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m}) of the given parameter vector. |
| check_constraints | Check the constraint matrix has the correct form |
| check_data | Check the data is in the correct form |
| check_gmvar | Checks whether the given object has class attribute "gmvar" |
| check_null_data | Checks whether the given object contains data or not |
| check_parameters | Check that the given parameter vector satisfies model assumptions |
| check_pMd | Check that p, M and d are correctly set |
| diagnostic_plot | Quantile residual diagnostic plot for GMVAR model |
| dlogmultinorm | Calculate logarithms of multiple multivariate normal densities with varying mean and constant covariance matrix |
| eurusd | Euro area and U.S. long-term government bond yields and Euro-U.S. dollar exchange rate. |
| fitGMVAR | Two-phase maximum likelihood estimation of GMVAR model |
| format_valuef | Function factory for value formatting |
| form_boldA | Form the ((dp)x(dp)) "bold A" matrices related to the VAR processes |
| GAfit | Genetic algorithm for preliminary estimation of GMVAR model |
| get_boldA_eigens | Calculate absolute values of the eigenvalues of the "bold A" matrices containing the AR coefficients |
| get_gradient | Calculate gradient or Hessian matrix |
| get_hessian | Calculate gradient or Hessian matrix |
| get_IC | Calculate AIC, HQIC and BIC |
| get_regime_means | Calculate and return regime means mu_{m} |
| get_regime_means_int | Calculate and return regime means mu_{m} |
| get_test_Omega | Compute covariance matrix Omega used in quantile residual tests |
| GMVAR | Create object of class 'gmvar' defining a GMVAR model |
| in_paramspace | Determine whether the parameter vector lies in the parameter space or not |
| in_paramspace_int | Determine whether the parameter vector lies in the parameter space or not |
| is_stationary | Check the stationary condition of given GMVAR model |
| iterate_more | Maximum likelihood estimation of GMVAR model with preliminary estimates |
| logLik.gmvar | Create object of class 'gmvar' defining a GMVAR model |
| loglikelihood | Compute log-likelihood of GMVAR model using parameter vector |
| loglikelihood_int | Compute the log-likelihood of Gaussian Mixture Vector Autoregressive model |
| n_params | Calculate the number of parameters in GMVAR model parameter vector |
| pick_allA | Pick coefficient all matrices |
| pick_all_phi0_A | Pick all phi_{m,0} or mu_{m} and A_{m} parameter values from the given parameter vector. |
| pick_alphas | Pick mixing weight parameters alpha_{m}, m=1,...,M from the given parameter vector. |
| pick_Am | Pick coefficient matrices |
| pick_Ami | Pick coefficient matrix |
| pick_Omegas | Pick covariance matrices |
| pick_phi0 | Pick phi_{m,0} or mu_{m}, m=1,..,M vectors from the given parameter vector |
| pick_regime | Pick regime parameters *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m}) from the given parameter vector. |
| plot.gmvar | Create object of class 'gmvar' defining a GMVAR model |
| plot.gmvarpred | plot method for class 'gmvarpred' objects |
| plot.qrtest | Quantile residual tests |
| predict.gmvar | Predict method for class 'gmvar' objects |
| print.gmvar | Create object of class 'gmvar' defining a GMVAR model |
| print.gmvarpred | print method for class 'gmvarpred' objects |
| print.gmvarsum | Summary print method from objects of class 'gmvarsum' |
| print.qrtest | Quantile residual tests |
| print_std_errors | Print standard errors of GMVAR model in the same form as the model estimates are printed |
| quantile_residuals | Calculate multivariate quantile residuals of GMVAR model |
| quantile_residuals_int | Calculate multivariate quantile residuals of GMVAR model |
| quantile_residual_tests | Quantile residual tests |
| random_coefmats | Create random VAR-model (dxd) coefficient matrices A. |
| random_coefmats2 | Create somewhat random stationary VAR model (dxd) coefficient matrices A. |
| random_covmat | Create somewhat random VAR model error term covariance matrix |
| random_ind | Create somewhat random mean-parametrized parameter vector of GMVAR model, that may not be stationary! |
| random_ind2 | Create somewhat random parameter vector of GMVAR model that is always stationary |
| reform_constrained_pars | Reform constrained parameter vector into the "standard" form |
| reform_data | Reform data |
| regime_distance | Calculate "distance" between two (scaled) regimes *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m}) |
| residuals.gmvar | Create object of class 'gmvar' defining a GMVAR model |
| simulateGMVAR | Simulate from GMVAR process |
| smart_covmat | Create somewhat random VAR-model (dxd) error term covariance matrix Omega fairly close to a given *positive definite* covariance matrix using (scaled) Wishart distribution. |
| smart_ind | Create somewhat random parameter vector of GMVAR model fairly close to a given parameter vector |
| sort_components | Sort components in parameter vector by mixing weights into a decreasing order |
| standard_errors | Calculate standard errors for estimates of GMVAR model |
| summary.gmvar | Create object of class 'gmvar' defining a GMVAR model |
| swap_parametrization | Swap the parametrization of object of class 'gmvar' defining a GMVAR model |
| unvec | Reverse vectorization operator. |
| unvech | Reverse operator of the parsimonious vectorization operator 'vech()'. |
| vec | Vectorization operator. |
| vech | Parsimonious vectorization operator for symmetric matrices. |