bvhar
provides functions to analyze multivariate time series time series using
Basically, the package focuses on the research with forecasting.
You can install the development version from develop branch.
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() |
Minnesota |
BVHAR | bvhar_minnesota() |
Minnesota |
BVAR-SV | bvar_sv() |
SSVS, Horseshoe |
BVHAR-SV | bvhar_sv() |
SSVS, Horseshoe |
This readme document shows forecasting procedure briefly. Details about each function are in vignettes and help documents.
h-step ahead forecasting:
h <- 19
etf_split <- divide_ts(etf_vix, h) # Try ?divide_ts
etf_tr <- etf_split$train
etf_te <- etf_split$test
VAR(5):
Forecasting:
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
#> 10.647
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
#> 3.84
Minnesota 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
#> # Type '?bvar_minnesota' in the console for some help.
#> ========================================================
#>
#> 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
MSE:
forecast_bvar <- predict(mod_bvar, h)
(msebvar <- mse(forecast_bvar, etf_te))
#> GVZCLS OVXCLS VXFXICLS VXEEMCLS VXSLVCLS EVZCLS VXXLECLS VXGDXCLS
#> 4.463 13.510 1.336 11.267 9.802 0.862 21.929 5.418
#> VXEWZCLS
#> 7.362
BVHAR-S:
(bvhar_spec_v1 <- set_bvhar(sig, lam, delta, eps))
#> Model Specification for BVHAR
#>
#> Parameters: Coefficent matrice and Covariance matrix
#> Prior: MN_VAR
#> # Type '?bvhar_minnesota' in the console for some help.
#> ========================================================
#>
#> 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
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
#> 3.58 4.76 1.32 5.71 6.29 1.15 14.03 2.52
#> VXEWZCLS
#> 5.41
BVHAR-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
#> # Type '?bvhar_minnesota' in the console for some help.
#> ========================================================
#>
#> 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
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 4.39 1.37 5.63 6.16 1.19 14.18 2.52
#> VXEWZCLS
#> 5.23
autoplot(forecast_var, x_cut = 870, ci_alpha = .7, type = "wrap") +
autolayer(forecast_vhar, ci_alpha = .6) +
autolayer(forecast_bvar, ci_alpha = .4) +
autolayer(forecast_bvhar_v1, ci_alpha = .2) +
autolayer(forecast_bvhar_v2, ci_alpha = .1)
Please cite this package with following BibTeX:
@Manual{,
title = {{bvhar}: Bayesian Vector Heterogeneous Autoregressive Modeling},
author = {Young Geun Kim and Changryong Baek},
year = {2023},
note = {R package version 2.0.1},
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 = {2023},
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.