Function reference • bvhar

The bvhar package

bvhar bvhar-package: bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

VAR

Vector autoregressive model.

var_lm() print(<varlse>) knit_print(<varlse>): Fitting Vector Autoregressive Model of Order p Model

VARtoVMA(): Convert VAR to VMA(infinite)

VHAR

Vector heterogeneous autoregressive model.

vhar_lm() print(<vharlse>) knit_print(<vharlse>): Fitting Vector Heterogeneous Autoregressive Model

VHARtoVMA(): Convert VHAR to VMA(infinite)

Bayesian Model

set_bvar() set_bvar_flat() set_bvhar() set_weight_bvhar() print(<bvharspec>) knit_print(<bvharspec>): Hyperparameters for Bayesian Models

init_ssvs() print(<ssvsinit>) knit_print(<ssvsinit>): Initial Parameters of Stochastic Search Variable Selection (SSVS) Model

set_ssvs() print(<ssvsinput>) knit_print(<ssvsinput>): Stochastic Search Variable Selection (SSVS) Hyperparameter for Coefficients Matrix and Cholesky Factor

set_lambda() set_psi() print(<bvharpriorspec>) knit_print(<bvharpriorspec>): Hyperpriors for Bayesian Models

set_horseshoe() print(<horseshoespec>) knit_print(<horseshoespec>): Horseshoe Prior Specification

set_sv() print(<svspec>): Stochastic Volatility Specification

set_intercept() print(<interceptspec>) knit_print(<interceptspec>): Prior for Constant Term

BVAR

Bayesian VAR model.

bvar_minnesota() print(<bvarmn>) knit_print(<bvarmn>): Fitting Bayesian VAR(p) of Minnesota Prior

bvar_flat() print(<bvarflat>) knit_print(<bvarflat>): Fitting Bayesian VAR(p) of Flat Prior

bvar_niwhm() print(<bvarhm>) knit_print(<bvarhm>): Fitting Hierarchical Bayesian VAR(p)

bvar_sv() print(<bvarsv>) knit_print(<bvarsv>): Fitting Bayesian VAR-SV

bvar_ssvs() print(<bvarssvs>) knit_print(<bvarssvs>): Fitting Bayesian VAR(p) of SSVS Prior

bvar_horseshoe() print(<bvarhs>) knit_print(<bvarhs>): Fitting Bayesian VAR(p) of Horseshoe Prior

BVHAR

Bayesian VHAR model.

bvhar_minnesota() print(<bvharmn>) knit_print(<bvharmn>): Fitting Bayesian VHAR of Minnesota Prior

bvhar_sv() print(<bvharsv>) knit_print(<bvharsv>): Fitting Bayesian VHAR-SV

bvhar_ssvs() print(<bvharssvs>) knit_print(<bvharssvs>): Fitting Bayesian VHAR of SSVS Prior

bvhar_horseshoe() print(<bvharhs>) knit_print(<bvharhs>): Fitting Bayesian VHAR of Horseshoe Prior

Summary method

summary(<varlse>) print(<summary.varlse>) knit_print(<summary.varlse>): Summarizing Vector Autoregressive Model

summary(<vharlse>) print(<summary.vharlse>) knit_print(<summary.vharlse>): Summarizing Vector HAR Model

summary(<normaliw>) print(<summary.normaliw>) knit_print(<summary.normaliw>): Summarizing Bayesian Multivariate Time Series Model

print(<summary.bvharsp>) knit_print(<summary.ssvsmod>) summary(<ssvsmod>) summary(<hsmod>): Summarizing BVAR and BVHAR with Shrinkage Priors

Forecasting

predict(<varlse>) predict(<vharlse>) predict(<bvarmn>) predict(<bvharmn>) predict(<bvarflat>) predict(<bvarssvs>) predict(<bvharssvs>) predict(<bvarhs>) predict(<bvharhs>) predict(<bvarsv>) predict(<bvharsv>) print(<predbvhar>) knit_print(<predbvhar>): Forecasting Multivariate Time Series

divide_ts(): Split a Time Series Dataset into Train-Test Set

forecast_roll() print(<bvharcv>) knit_print(<bvharcv>): Out-of-sample Forecasting based on Rolling Window

forecast_expand(): Out-of-sample Forecasting based on Expanding Window

Evaluation

mse(): Evaluate the Model Based on MSE (Mean Square Error)

mae(): Evaluate the Model Based on MAE (Mean Absolute Error)

mape(): Evaluate the Model Based on MAPE (Mean Absolute Percentage Error)

mase(): Evaluate the Model Based on MASE (Mean Absolute Scaled Error)

mrae(): Evaluate the Model Based on MRAE (Mean Relative Absolute Error)

relmae(): Evaluate the Model Based on RelMAE (Relative MAE)

rmsfe(): Evaluate the Model Based on RMSFE

rmafe(): Evaluate the Model Based on RMAFE

rmape(): Evaluate the Model Based on RMAPE (Relative MAPE)

rmase(): Evaluate the Model Based on RMASE (Relative MASE)

conf_fdr(): Evaluate the Sparsity Estimation Based on FDR

conf_fnr(): Evaluate the Sparsity Estimation Based on FNR

conf_fscore(): Evaluate the Sparsity Estimation Based on F1 Score

conf_prec(): Evaluate the Sparsity Estimation Based on Precision

conf_recall(): Evaluate the Sparsity Estimation Based on Recall

confusion(): Evaluate the Sparsity Estimation Based on Confusion Matrix

fromse(): Evaluate the Estimation Based on Frobenius Norm

spne(): Evaluate the Estimation Based on Spectral Norm Error

relspne(): Evaluate the Estimation Based on Relative Spectral Norm Error

lpl(): Evaluate the Model Based on Log Predictive Likelihood

Tuning

compute_logml(): Extracting Log of Marginal Likelihood

choose_bvar() choose_bvhar() print(<bvharemp>) knit_print(<bvharemp>): Finding the Set of Hyperparameters of Individual Bayesian Model

bound_bvhar() print(<boundbvharemp>) knit_print(<boundbvharemp>): Setting Empirical Bayes Optimization Bounds

choose_bayes(): Finding the Set of Hyperparameters of Bayesian Model

choose_ssvs(): Choose the Hyperparameters Set of SSVS-VAR using a Default Semiautomatic Approach

Information criteria

AIC(<varlse>) AIC(<vharlse>) AIC(<bvarmn>) AIC(<bvarflat>) AIC(<bvharmn>): Akaike's Information Criterion of Multivariate Time Series Model

BIC(<varlse>) BIC(<vharlse>) BIC(<bvarmn>) BIC(<bvarflat>) BIC(<bvharmn>): Bayesian Information Criterion of Multivariate Time Series Model

FPE(): Final Prediction Error Criterion

FPE(<varlse>) FPE(<vharlse>): Final Prediction Error Criterion of Multivariate Time Series Model

HQ(): Hannan-Quinn Criterion

HQ(<varlse>) HQ(<vharlse>) HQ(<bvarmn>) HQ(<bvarflat>) HQ(<bvharmn>): Hannan-Quinn Criterion of Multivariate Time Series Model

logLik(<varlse>) logLik(<vharlse>) logLik(<bvarmn>) logLik(<bvarflat>) logLik(<bvharmn>): Extract Log-Likelihood of Multivariate Time Series Model

choose_var(): Choose the Best VAR based on Information Criteria

compute_dic(): Deviance Information Criterion of Multivariate Time Series Model

Plots

autoplot(<normaliw>): Residual Plot for Minnesota Prior VAR Model

autoplot(<summary.normaliw>): Density Plot for Minnesota Prior VAR Model

autoplot(<predbvhar>) autolayer(<predbvhar>): Plot Forecast Result

geom_eval(): Adding Test Data Layer

gg_loss(): Compare Lists of Models

autoplot(<bvharirf>): Plot Impulse Responses

autoplot(<bvharsp>): Plot the Result of BVAR and BVHAR MCMC

autoplot(<summary.bvharsp>): Plot the Heatmap of SSVS Coefficients

Simulation and Random Generation

sim_var(): Generate Multivariate Time Series Process Following VAR(p)

sim_vhar(): Generate Multivariate Time Series Process Following VAR(p)

sim_mncoef(): Generate Minnesota BVAR Parameters

sim_mnvhar_coef(): Generate Minnesota BVAR Parameters

sim_mnormal(): Generate Multivariate Normal Random Vector

sim_matgaussian(): Generate Matrix Normal Random Matrix

sim_iw(): Generate Inverse-Wishart Random Matrix

sim_mniw(): Generate Normal-IW Random Family

sim_mvt(): Generate Multivariate t Random Vector

sim_ssvs_var() sim_ssvs_vhar(): Generate SSVS Parameters

sim_horseshoe_var() sim_horseshoe_vhar(): Generate Horseshoe Parameters

Data

etf_vix: CBOE ETF Volatility Index Dataset

oxfordman_rv oxfordman_rk: Oxford-Man Institute Realized Library

Other generic functions

print(<bvharirf>) analyze_ir() knit_print(<bvharirf>): Impulse Response Analysis

stableroot(): Roots of characteristic polynomial

stableroot(<varlse>) stableroot(<vharlse>) stableroot(<bvarmn>) stableroot(<bvarflat>) stableroot(<bvharmn>): Characteristic polynomial roots for VAR Coefficient Matrix

is.stable(): Stability of the process

is.stable(<varlse>) is.stable(<vharlse>) is.stable(<bvarmn>) is.stable(<bvarflat>) is.stable(<bvharmn>): Stability of VAR Coefficient Matrix

coef(<varlse>) coef(<vharlse>) coef(<bvarmn>) coef(<bvarflat>) coef(<bvharmn>) coef(<bvharsp>) coef(<summary.bvharsp>): Coefficient Matrix of Multivariate Time Series Models

residuals(<varlse>) residuals(<vharlse>) residuals(<bvarmn>) residuals(<bvarflat>) residuals(<bvharmn>): Residual Matrix from Multivariate Time Series Models

fitted(<varlse>) fitted(<vharlse>) fitted(<bvarmn>) fitted(<bvarflat>) fitted(<bvharmn>): Fitted Matrix from Multivariate Time Series Models

is.varlse() is.vharlse() is.bvarmn() is.bvarflat() is.bvharmn() is.predbvhar() is.bvharcv() is.bvharspec() is.bvharpriorspec() is.bvharemp() is.boundbvharemp() is.interceptspec() is.ssvsinput() is.ssvsinit() is.bvharpriorspec() is.horseshoespec() is.svspec(): See if the Object a class in this package

split_coef(): Splitting Coefficient Matrix into List