This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the...

A new state space approach is proposed to model the time-dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either follow an autoregressive (AR) process or...

This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the...

A Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student’s t-error distribution is described where we first consider an asymmetric heavy-tailed error and leverage effects. An efficient Markov chain Monte Carlo estimation method is described that...

This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables...

This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables...

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student's t-error distribution is described where we first consider an asymmetric heavy-tailness as well as leverage effects. An efficient Markov chain Monte Carlo estimation method is described exploiting...

A new state space approach is proposed to model the time- dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either follow an autoregressive (AR) process or...