BVAR-package |
BVAR: Hierarchical Bayesian vector autoregression |
as.mcmc.bvar |
Methods for 'coda' Markov chain Monte Carlo objects |
as.mcmc.bvar_chains |
Methods for 'coda' Markov chain Monte Carlo objects |
BVAR |
BVAR: Hierarchical Bayesian vector autoregression |
bvar |
Hierarchical Bayesian vector autoregression |
bv_alpha |
Minnesota prior settings |
bv_dummy |
Dummy prior settings |
bv_fcast |
Forecast settings |
bv_irf |
Impulse response settings and identification |
bv_lambda |
Minnesota prior settings |
bv_metropolis |
Metropolis-Hastings settings |
bv_mh |
Metropolis-Hastings settings |
bv_minnesota |
Minnesota prior settings |
bv_mn |
Minnesota prior settings |
bv_priors |
Prior settings |
bv_psi |
Minnesota prior settings |
bv_soc |
Dummy prior settings |
bv_sur |
Dummy prior settings |
coda |
Methods for 'coda' Markov chain Monte Carlo objects |
coef.bvar |
Coefficient and VCOV methods for Bayesian VARs |
companion |
Retrieve companion matrix from a Bayesian VAR |
companion.bvar |
Retrieve companion matrix from a Bayesian VAR |
companion.default |
Retrieve companion matrix from a Bayesian VAR |
density.bvar |
Density methods for Bayesian VARs |
fevd |
Impulse response and forecast error methods for Bayesian VARs |
fevd.bvar |
Impulse response and forecast error methods for Bayesian VARs |
fevd.default |
Impulse response and forecast error methods for Bayesian VARs |
fevd<- |
Impulse response and forecast error methods for Bayesian VARs |
fitted.bvar |
Fitted and residual methods for Bayesian VARs |
fred_code |
FRED transformation and subset helper |
fred_md |
FRED-MD and FRED-QD: Databases for Macroeconomic Research |
fred_qd |
FRED-MD and FRED-QD: Databases for Macroeconomic Research |
fred_transform |
FRED transformation and subset helper |
hist_decomp |
Historical decomposition |
hist_decomp.bvar |
Historical decomposition |
hist_decomp.default |
Historical decomposition |
independent_index |
Density methods for Bayesian VARs |
irf |
Impulse response and forecast error methods for Bayesian VARs |
irf.bvar |
Impulse response and forecast error methods for Bayesian VARs |
irf.default |
Impulse response and forecast error methods for Bayesian VARs |
irf<- |
Impulse response and forecast error methods for Bayesian VARs |
logLik.bvar |
Log-Likelihood method for Bayesian VARs |
lps |
Model fit in- and out-of-sample |
lps.bvar |
Model fit in- and out-of-sample |
lps.default |
Model fit in- and out-of-sample |
par_bvar |
Parallel hierarchical Bayesian vector autoregression |
plot.bvar |
Plotting method for Bayesian VARs |
plot.bvar_density |
Density methods for Bayesian VARs |
plot.bvar_fcast |
Plotting method for Bayesian VAR predictions |
plot.bvar_irf |
Plotting method for Bayesian VAR impulse responses |
plot.bvar_resid |
Fitted and residual methods for Bayesian VARs |
predict.bvar |
Predict method for Bayesian VARs |
predict<- |
Predict method for Bayesian VARs |
residuals.bvar |
Fitted and residual methods for Bayesian VARs |
rmse |
Model fit in- and out-of-sample |
rmse.bvar |
Model fit in- and out-of-sample |
rmse.default |
Model fit in- and out-of-sample |
summary.bvar |
Summary method for Bayesian VARs |
summary.bvar_fcast |
Predict method for Bayesian VARs |
summary.bvar_irf |
Impulse response and forecast error methods for Bayesian VARs |
vcov.bvar |
Coefficient and VCOV methods for Bayesian VARs |
WAIC |
Widely applicable information criterion (WAIC) for Bayesian VARs |
WAIC.bvar |
Widely applicable information criterion (WAIC) for Bayesian VARs |
WAIC.default |
Widely applicable information criterion (WAIC) for Bayesian VARs |