| add_data | Add data to an object of class 'gmvar' defining a GMVAR model |
| all_pos_ints | Check whether all arguments are positive integers |
| alt_gmvar | Construct a GMVAR model based on results from an arbitrary estimation round of 'fitGMVAR' |
| calc_gradient | Calculate gradient or Hessian matrix |
| calc_hessian | Calculate gradient or Hessian matrix |
| change_parametrization | Change parametrization of a 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 |
| check_parameters | Check that the given parameter vector satisfies the model assumptions |
| check_pMd | Check that p, M, and d are correctly set |
| cond_moments | Compute conditional moments of a GMVAR model |
| diagnostic_plot | Quantile residual diagnostic plot for a 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 a 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 a GMVAR model |
| get_boldA_eigens | Calculate absolute values of the eigenvalues of the "bold A" matrices containing the AR coefficients |
| get_foc | Calculate gradient or Hessian matrix |
| get_gradient | Calculate gradient or Hessian matrix |
| get_hessian | Calculate gradient or Hessian matrix |
| get_IC | Calculate AIC, HQIC, and BIC |
| get_minval | Returns the default smallest allowed log-likelihood for given data. |
| get_omega_eigens | Calculate the eigenvalues of the "Omega" error term covariance matrices |
| get_regime_autocovs | Calculate regimewise autocovariance matrices |
| get_regime_autocovs_int | Calculate regimewise autocovariance matrices |
| get_regime_means | Calculate regime means mu_{m} |
| get_regime_means_int | Calculate regime means mu_{m} |
| get_soc | Calculate gradient or Hessian matrix |
| get_test_Omega | Compute covariance matrix Omega used in quantile residual tests |
| GMVAR | Create a class 'gmvar' object defining a GMVAR model |
| gmvarkit | gmvarkit: Estimate Gaussian Mixture Vector Autoregressive (GMVAR) model |
| in_paramspace | Determine whether the parameter vector lies in the parameter space |
| in_paramspace_int | Determine whether the parameter vector lies in the parameter space |
| is_stationary | Check the stationary condition of a given GMVAR model |
| iterate_more | Maximum likelihood estimation of a GMVAR model with preliminary estimates |
| logLik.gmvar | Create a class 'gmvar' object defining a GMVAR model |
| loglikelihood | Compute log-likelihood of a GMVAR model using parameter vector |
| loglikelihood_int | Compute log-likelihood of a 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 |
| pick_alphas | Pick mixing weight parameters alpha_{m}, m=1,...,M |
| 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 |
| pick_regime | Pick regime parameters *upsilon_{m}* = (phi_{m,0},*phi_{m}*,sigma_{m}) |
| plot.gmvar | Create a class 'gmvar' object 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 a class 'gmvar' object 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 |
| profile_logliks | Plot profile log-likehoods around the estimates |
| 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 random stationary VAR model (dxd) coefficient matrices A. |
| random_covmat | Create random VAR model error term covariance matrix |
| random_ind | Create random mean-parametrized parameter vector of a GMVAR model that may not be stationary |
| random_ind2 | Create somewhat random parameter vector of a 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 a class 'gmvar' object defining a GMVAR model |
| simulateGMVAR | Simulate from GMVAR process |
| smart_covmat | Create 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 random parameter vector of a GMVAR model fairly close to a given parameter vector |
| sort_components | Sort components in parameter vector according to mixing weights into a decreasing order |
| standard_errors | Calculate standard errors for estimates of GMVAR model |
| summary.gmvar | Create a class 'gmvar' object defining a GMVAR model |
| swap_parametrization | Swap the parametrization of a GMVAR model |
| uncond_moments | Calculate the unconditional mean, variance, the first p autocovariances, and the first p autocorrelations of a GMVAR process |
| uncond_moments_int | Calculate the unconditional mean, variance, the first p autocovariances, and the first p autocorrelations of a GMVAR process |
| unvec | Reverse vectorization operator |
| unvech | Reverse operator of the parsimonious vectorization operator 'vech' |
| vec | Vectorization operator |
| vech | Parsimonious vectorization operator for symmetric matrices |