This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic...

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

Two commonly-used criteria for evaluating voting rules are how infrequently the rules provide opportunities for strategic voting and how infrequently they encounter voting paradoxes. The lack of ranking data from enough actual elections to determine these frequencies with reasonable accuracy...

It has recently been suggested that speculation is now playing an important role in daily price movements of global oil prices. This raises the question: what are important drivers of price changes given this new 'speculative' regime? We identify new factors of the oil market related to...

This study tested the unbiased pricing hypothesis in the copper, aluminum, nickel, and lead markets for the period October 2011 to May 2021. Wavelets and a time-varying parameter model with Bayesian priors were the primary tools. Each metal market was found to be weak market efficient, the...

Trade in value added (TiVA) indicators are increasingly used to monitor countries' integration into global supply chains. However, they are published with a significant lag - often two or three years - which reduces their relevance for monitoring recent economic developments. This paper aims to...

The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor...