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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.