TVPVAR: Time-varying parameter vector autoregression

In ConnectednessApproach: Connectedness Approach

Time-varying parameter vector autoregression

Description

Estimate TVP-VAR model

Usage

TVPVAR(x, configuration = list(l = c(0.99, 0.99), nlag = 1, prior = NULL))

Arguments

x	zoo data matrix
configuration	model configuration
nlag	Lag length
prior	List of prior VAR coefficients and variance-covariance matrix
l	forgetting factors (kappa1, kappa2)

Value

Estimate TVP-VAR model

Author(s)

David Gabauer

References

Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116.

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.

Examples

data(dy2012)
prior = BayesPrior(dy2012, nlag=1)
fit = TVPVAR(dy2012, configuration=list(nlag=1, prior=prior, l=c(0.99,0.99)))

ConnectednessApproach documentation built on Aug. 31, 2022, 5:05 p.m.