This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence …, relative risk and odds ratio. The performance of our preferred algorithm is demonstrated in multiple simulation studies and …

Probabilistic risk beliefs are key drivers of economic and health decisions, but people are not always certain about … measurable separately from the levels of risk beliefs. People with higher levels of imprecision update their beliefs more in … can map our data onto both a standard Bayesian model and a version that is designed to handle imprecise probabilities …

increases in risk taking. Where we can separately identify changes in risk-independent performance and risk taking, our … increases in risk taking. These effects are concentrated among those closest to the margin of elimination and among lower …

households reduce their financial risk exposure when confronted with background risk. Our novel modelling approach - termed a … risk, and is unique in recovering for, any given risky asset class, the shares that are reallocated to a safer asset … category. Background risk exerts a significant impact on household portfolios, resulting in a 'flight from risk', away from …

take risk as measured by the general risk question. We demonstrate that this disposition, which we call risk conception, is … strongly associated with optimism, a stable facet of personality and that it predicts real-life risk taking. The general risk … question captures this disposition alongside pure risk preference. This enlightens why the general risk question is a better …

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by …