We compare a number of GARCH and stochastic volatility (SV) models using nine series of
oil, petroleum product and natural gas prices in a formal Bayesian model comparison …

We develop importance sampling methods for computing two popular Bayesian model
comparison criteria, namely, the marginal likelihood and the deviance information criterion …

Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs)
suffer from the limited number of samples and simplified features, so as to produce poor …

We employ a class of time-varying Bayesian vector autoregressive (VAR) models on new
standard dataset of China's GDP constructed by Chang et al.(2015) to examine the …

We examine and compare a large number of generalized autoregressive conditional
heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and …

Abstract Given Pacific Alliance (PA) countries' dependence on the external sector amidst
volatile global financial conditions and increased trade openness, it is important to …

When quantifying the importance of supply and demand for oil price fluctuations, a wide
range of estimates have been reported. Models identified via a sharp upper bound on the …

We propose importance sampling algorithms based on fast band matrix routines for
estimating the observed-data likelihoods for a variety of stochastic volatility models. This is …

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing
price movements across all the United States (US) states plus District of Columbia (DC). We …

The deviance information criterion (DIC) has been widely used for Bayesian model
comparison. However, recent studies have cautioned against the use of certain variants of …