This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally...

We explore the application of integrated nested Laplace approximations for the Bayesian estimation of stochastic volatility models characterized by long memory. The logarithmic variance persistence in these models is represented by a Fractional Gaussian Noise process, which we approximate as a...

The output gap, while inherently unobservable, plays a pivotal role in informing policymakers due to its significant implications for forecasting inflation rates and understanding the mechanisms of monetary policy transmission. Traditional filters frequently employed in estimating the output gap...