This paper investigates the use of DMA approach for identifying good inflation predictors and forecasting inflation in Mongolia, one of the most commodity dependent economies, using dynamic model averaging (DMA). The DMA approach allows for both set of predictors for inflation and marginal...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...

This chapter proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation to...

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the...

The volatility of 19 agricultural commodity prices are examined at monthly and annual frequencies. All of the price series are found to exhibit persistent volatility (periods of relatively high and low volatility). There is also strong evidence of transmission of volatilities across prices....

We apply a recent methodology, Bayesian stochastic model specification search (SMSS), for the selection of the unobserved components (level, slope, seasonal cycles, trading days effects) that are stochastically evolving over time. SMSS hinges on two basic ingredients: the non-centered...

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...

This chapter proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation to...

We construct one triple-threshold GARCH model to analyze the asymmetric response of mean and conditional volatility. In parameter estimation, we apply Griddy-Gibbs sampling method, which require less work in selection of starting values and pre-run. As we apply this model in Chinese stock...