calibrated forecasts compared to maximum likelihood estimation. From a theoretical perspective, both estimators are consistent …

provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts …

stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using … scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail …

density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series … forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region …. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters …

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with …

able to account for site-specific local features leading to strongly skewed residuals. This residual skewness remains even … model is proposed to overcome this problem by assuming a skewed response distribution to account for possible skewness. This …