We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

We develop an axiomatic foundation for the classical problem of paying workers in settings where their actions are not observed by the employer. The latter only observes the distribution of ability and demands fairness. First, we uniquely characterize workers’ expected pay thanks to a set of...

This paper analyzes the root cause of Ellsberg-type choices. This class of problems shares the feature that at the time of the decision, t=m, the decision maker (DM) possesses partial information about the events/propositions of interest: DM knows the objective probabilities of some sub-class...

We consider social choice functions (SCFs) that are locally robust ordinal Bayesian incentive compatible (LOBIC) with respect to correlated priors. We model such priors using a betweenness property and assume the coexistence of both positively and negatively correlated priors. We introduce the...

Biochar adsorption is one of the most effective methods for mitigating heavy metal pollution; however, it remains quite challenging for its limited adsorption capacity and selectivity. To address these issues and enhance the adsorption performance of biochar for heavy metals in soil, we proposed...

The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting...

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the...

The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their...

We propose a Bayesian VAR model with stochastic volatility and time varying skewness to estimate the degree of labour at risk in the euro area and in the United States. We model the asymmetry of the shocks to changes in the unemployment rate as a function of real activity and financial risk...

This study examines whether and how important it is to adjust output gap frameworks during the COVID-19 pandemic and similar unprecedentedly large-scale episodes. Our proposed modelling framework comprises a Bayesian Structural Vector Autoregresion with an identification setup based on a...