varGuidTS: Variance-Guided Time-Series Modeling for Temporal Risk Detection
Fits balanced-panel autoregressive models with conditional
heteroscedasticity for temporal risk detection. The main estimator combines
autoregressive exogenous mean modeling with GARCH-X variance modeling,
subject-specific baseline terms, shared population coefficients, and L1
penalization for high-dimensional covariates. The package returns
conditional mean and variance estimates, coefficient summaries, simulations,
and exceedance-based risk scores defined as estimated conditional
threshold-exceedance probabilities. The implementation builds on the lasso
of Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
generalized autoregressive conditional heteroscedasticity of Bollerslev
(1986) <doi:10.1016/0304-4076(86)90063-1>, and L1-regularized
high-dimensional time-series modeling of Medeiros and Mendes (2016)
<doi:10.1016/j.jeconom.2015.10.011>.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=varGuidTS
to link to this page.