tvgarch
Time Varying GARCH Modelling
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.4.3 |
rolling source/ R- | tvgarch_2.4.3.tar.gz |
35.9 KiB |
2.4.3 |
rolling linux/jammy R-4.5 | tvgarch_2.4.3.tar.gz |
240.9 KiB |
2.4.3 |
latest source/ R- | tvgarch_2.4.3.tar.gz |
35.9 KiB |
2.4.3 |
latest linux/jammy R-4.5 | tvgarch_2.4.3.tar.gz |
240.9 KiB |
2.4.3 |
2026-04-23 source/ R- | tvgarch_2.4.3.tar.gz |
35.9 KiB |
2.4.3 |
2026-04-09 windows/windows R-4.5 | tvgarch_2.4.3.zip |
243.0 KiB |
2.4.2 |
2025-04-20 source/ R- | tvgarch_2.4.2.tar.gz |
36.5 KiB |