bvhar 2.1.0

• Use Signal Adaptive Variable Selector (SAVS) to generate sparse coefficient from shrinkage priors.

• var_bayes() and vhar_bayes() now handle both shrinkage priors and stochastic volatility.

• bvar_ssvs(), bvar_horseshoe(), bvar_sv(), bvhar_ssvs(), bvhar_horseshoe(), and bvhar_sv() are deprecated, and will be removed in v2.1.0 with their source functions.

• set_horseshoe() has additional setting for group_shrinkage. Horseshoe sampling now has additional group shrinkage level parameters.

• set_ssvs() now additionally should specify different Beta hyperparameters for each own-lag and cross-lag.

• set_ssvs() sets scaling factor and inverse-gamma hyperparameters for coefficients and cholesky factor slab sd.

• Use full bayesian approach to SSVS spike and slab sd's instead of semi-automatic approach, in var_bayes() and vhar_bayes().

• MCMC functions return give $param and $param_names, not individual $*_record members.

• sim_gig() generates Generalized Inverse Gaussian (GIG) random numbers using the algorithm of R package GIGrvg.

New priors

• set_dl() specifies Dirichlet-Laplace (DL) prior in var_bayes() and vhar_bayes().

• set_ng() specifies Normal-Gamma (NG) prior in var_bayes() and vhar_bayes().

• bvar_sv() and bvhar_sv() supports hierarchical Minnesota prior.

Internal changes

• Added regularization step in internal Normal posterior generation function against non-existing LLT case.

• Added BOOST_DISABLE_ASSERTS flag against boost asserts.

Spillover effects

• spillover() computes static spillover given model.

• dynamic_spillover() computes dynamic spillover given model.

Forecasting

• predict(), forecast_roll(), and forecast_expand() with LDLT models can use CI level when adding sparsity.

• predict(), forecast_roll(), and forecast_expand() of ldltmod have sparse option to use sparsity.

• predict(), forecast_roll(), and forecast_expand() with SV models can use CI level when adding sparsity.

• predict(), forecast_roll(), and forecast_expand() of svmod have sparse option to use sparsity.

• Out-of-sample forecasting functions are now S3 generics (forecast_roll() and forecast_expand()).

• Add Rolling-window forecasting for LDLT models (forecast_roll.ldltmod()).

• Add Expanding-window forecasting for LDLT models (forecast_expand.ldltmod()).

• Add Rolling-window forecasting for SV models (forecast_roll.svmod()).

• Add Expanding-window forecasting for SV models (forecast_expand.svmod()).

• When forecasting SV models, it is available to choose whether to use time-varying covariance (use_sv option, which is TRUE by default).

• forecast_roll() and forecast_expand() can implement OpenMP multithreading, except in bvarflat class.

• If the model uses multiple chain MCMC, static schedule is used in forecast_roll() and dynamic schedule in forecast_expand().

• sim_mniw() output format has been changed into list of lists.

• Now can use MNIW generation by including header (std::vector<Eigen::MatrixXd> sim_mn_iw(...)).

• Compute LPL inside forecast_roll.svmod() and forecast_expand.svmod() using lpl option.

• Instead, lpl method is removed.