bvhar provides functions to analyze and forecast
multivariate time series using
Basically, the package focuses on the research with forecasting.
install.packages("bvhar")
You can install the development version from develop branch.
# install.packages("remotes")
remotes::install_github("ygeunkim/bvhar@develop")We started to develop a Python version in python directory.
library(bvhar) # this package
library(dplyr)Repeatedly, bvhar is a research tool to analyze
multivariate time series model above
| Model | function | prior |
|---|---|---|
| VAR | var_lm() |
|
| VHAR | vhar_lm() |
|
| BVAR | bvar_minnesota() and
choose_bvar() |
Minnesota (will move to
var_bayes()) |
| BVHAR | bvhar_minnesota() and
choose_bvhar() |
Minnesota (will move to
vhar_bayes()) |
| BVAR | var_bayes() |
SSVS, Horseshoe, Minnesota, NG, DL, GDP |
| BVHAR | vhar_bayes() |
SSVS, Horseshoe, Minnesota, NG, DL, GDP |
This readme document shows forecasting procedure briefly. Details
about each function are in vignettes and help documents. Note that each
bvar_minnesota() and bvhar_minnesota() will be
integrated into var_bayes() and vhar_bayes()
and removed in the near future.
h-step ahead forecasting:
h <- 19
etf_split <- divide_ts(etf_vix, h) # Try ?divide_ts
etf_tr <- etf_split$train
etf_te <- etf_split$testVAR(5):
mod_var <- var_lm(y = etf_tr, p = 5)Forecasting:
forecast_var <- predict(mod_var, h)MSE:
(msevar <- mse(forecast_var, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 5.381 14.689 2.838 9.451 10.078 0.654 22.436 9.992
#> VXEWZCLS
#> 10.647mod_vhar <- vhar_lm(y = etf_tr)MSE:
forecast_vhar <- predict(mod_vhar, h)
(msevhar <- mse(forecast_vhar, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 6.15 2.49 1.52 1.58 10.55 1.35 8.79 4.43
#> VXEWZCLS
#> 3.84Minnesota prior:
lam <- .3
delta <- rep(1, ncol(etf_vix)) # litterman
sig <- apply(etf_tr, 2, sd)
eps <- 1e-04
(bvar_spec <- set_bvar(sig, lam, delta, eps))
#> Model Specification for BVAR
#>
#> Parameters: Coefficent matrice and Covariance matrix
#> Prior: Minnesota
#> ========================================================
#>
#> Setting for 'sigma':
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 3.77 10.63 3.81 4.39 5.99 2.27 4.88 7.45
#> VXEWZCLS
#> 7.03
#>
#> Setting for 'lambda':
#> [1] 0.3
#>
#> Setting for 'delta':
#> [1] 1 1 1 1 1 1 1 1 1
#>
#> Setting for 'eps':
#> [1] 1e-04
#>
#> Setting for 'hierarchical':
#> [1] FALSEmod_bvar <- bvar_minnesota(y = etf_tr, p = 5, bayes_spec = bvar_spec)MSE:
forecast_bvar <- predict(mod_bvar, h)
(msebvar <- mse(forecast_bvar, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 4.651 13.248 1.845 10.356 9.894 0.667 21.040 6.262
#> VXEWZCLS
#> 8.864BVHAR-S:
(bvhar_spec_v1 <- set_bvhar(sig, lam, delta, eps))
#> Model Specification for BVHAR
#>
#> Parameters: Coefficent matrice and Covariance matrix
#> Prior: MN_VAR
#> ========================================================
#>
#> Setting for 'sigma':
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 3.77 10.63 3.81 4.39 5.99 2.27 4.88 7.45
#> VXEWZCLS
#> 7.03
#>
#> Setting for 'lambda':
#> [1] 0.3
#>
#> Setting for 'delta':
#> [1] 1 1 1 1 1 1 1 1 1
#>
#> Setting for 'eps':
#> [1] 1e-04
#>
#> Setting for 'hierarchical':
#> [1] FALSEmod_bvhar_v1 <- bvhar_minnesota(y = etf_tr, bayes_spec = bvhar_spec_v1)MSE:
forecast_bvhar_v1 <- predict(mod_bvhar_v1, h)
(msebvhar_v1 <- mse(forecast_bvhar_v1, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 3.199 6.067 1.471 5.142 5.946 0.878 12.165 2.553
#> VXEWZCLS
#> 6.462BVHAR-L:
day <- rep(.1, ncol(etf_vix))
week <- rep(.1, ncol(etf_vix))
month <- rep(.1, ncol(etf_vix))
#----------------------------------
(bvhar_spec_v2 <- set_weight_bvhar(sig, lam, eps, day, week, month))
#> Model Specification for BVHAR
#>
#> Parameters: Coefficent matrice and Covariance matrix
#> Prior: MN_VHAR
#> ========================================================
#>
#> Setting for 'sigma':
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 3.77 10.63 3.81 4.39 5.99 2.27 4.88 7.45
#> VXEWZCLS
#> 7.03
#>
#> Setting for 'lambda':
#> [1] 0.3
#>
#> Setting for 'eps':
#> [1] 1e-04
#>
#> Setting for 'daily':
#> [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#>
#> Setting for 'weekly':
#> [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#>
#> Setting for 'monthly':
#> [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#>
#> Setting for 'hierarchical':
#> [1] FALSEmod_bvhar_v2 <- bvhar_minnesota(y = etf_tr, bayes_spec = bvhar_spec_v2)MSE:
forecast_bvhar_v2 <- predict(mod_bvhar_v2, h)
(msebvhar_v2 <- mse(forecast_bvhar_v2, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 3.63 3.85 1.64 5.12 5.75 1.08 13.60 2.58
#> VXEWZCLS
#> 5.54Please cite this package with following BibTeX:
@Manual{,
title = {{bvhar}: Bayesian Vector Heterogeneous Autoregressive Modeling},
author = {Young Geun Kim and Changryong Baek},
year = {2023},
doi = {10.32614/CRAN.package.bvhar},
note = {R package version 2.3.0},
url = {https://cran.r-project.org/package=bvhar},
}
@Article{,
title = {Bayesian Vector Heterogeneous Autoregressive Modeling},
author = {Young Geun Kim and Changryong Baek},
journal = {Journal of Statistical Computation and Simulation},
year = {2024},
volume = {94},
number = {6},
pages = {1139--1157},
doi = {10.1080/00949655.2023.2281644},
}Please note that the bvhar project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.