We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of...

This paper considers the problem of jointly decomposing a set of time series variablesinto cyclical and trend components, subject to sets of stochastic linear restrictionsamong these cyclical and trend components. We derive a closed form solution to anordinary problem featuring homogeneous...

This paper shows that expected uncertainty should be included as a key determinant in the derivation of the natural probability distribution of assets because it contains information that goes beyond information contained in state prices. I redefine the contingent state prices derived in the...

Mixed exponential distributions are frequently used in actuarial risk modeling. Distributions obtained through mixtures allow greater flexibility in the modeling of non-life insurance loss amounts . Several research works have studied mixed exponential distributions in univariate and...

Macroeconomic data have been shown to vary substantially between sources, especially so for low-income countries. While the impact of data revisions on inference is well documented for cross-country studies, there is no systematic analysis of the robustness of results obtained from time series...

The study examined stock prices (SP) and exchange rate (ER) interactions with multivariate VAR-GARCH model using monthly data from January 2000 to October 2014. The results of the Engle and Granger and Johansen cointegration test show that there is stable long-term relationship between SP and...