We propose a flexible, extensible family of distributions for testing Markov Chain Monte Carlo implementations. Distributions are created by nesting simple transformations, which allow various shapes, including multiple modes and fat tails. The resulting distributions can be sampled with high...

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically...

This study examines the relationship between environmental innovation and firm valuation between 2019 and 2023, within a broader framework that includes governance and macroeconomic variables. This study employs Bayesian model averaging and random forest methodologies to analyse how...

In this work, we study n-agent Bayesian games with m-dimensional vector types and linear payoffs, also called linear multidimensional Bayesian games. This class of games is equivalent with n-agent, m-game uniform multigames. We distinguish between games that have a discrete type space and those...

In a stage of more and more complex and high-frequency financial markets, the volatility analysis is a cornerstone of modern financial econometrics with practical applications in portfolio optimization, derivative pricing, and systematic risk assessment. This paper introduces a novel Bayesian...

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of...

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage...

This paper explores classical and Bayesian approaches to the estimation of unknown parameters and reliability functions for the power Rayleigh distribution. The maximum likelihood estimator (MLE) method is considered in classical estimation. The Bayesian estimation, on the other hand uses...

We present a comprehensive meta-analysis of the determinants of financial inclusion, synthesizing 3,817 estimates from 102 studies published between 2013 and 2024. To reconcile divergent findings, we convert all results to a common unbiased metric-the partial correlation coefficient corrected...

Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter...

This paper sets up a medium-scale small open economy TANK model with incomplete markets to address business cycles in Korea extensively. The estimated model via Bayesian estimation methodology shows that the fraction of households who lack access to financial markets increased from 30 percent to...

Modeling loss data is a crucial aspect of actuarial science. In the insurance industry, small claims occur frequently, while large claims are rare. Traditional heavy-tail distributions, such as Weibull, Log-Normal, and Inverse Gaussian distributions, are not suitable for describing insurance...

This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation...

The coral reef ecosystems of the Maldives are critical to the nation's ecological integrity, economic development, and climate resilience. As a small island state, the Maldives is heavily dependent on healthy reefs to support tourism, fishing, and coastal protection. However, these ecosystems...

This study applies a heteroscedastic spatial Durbin panel data model to investigate how sociodemographic and socioeconomic factors influence regional commuter mobility in the Greater Stockholm Area. Commuter mobility, defined as the flow of people to and from workplaces across regions and over...

This study investigates the determinants that drive the volatility of the credit default swaps (CDS) of BRICIT (Brazil, Russia, India, China, Indonesia, and Turkey) nations as a proxy measure for sovereign risk. On the existence of cointegration, an unrestricted error correction model integrated...

This paper considers Bayesian learning when players are biased about the data-generating process, and are biased about the opponent's bias about the data-generating process. Specifically, we assume that each player's bias about others takes the form of interpersonal projection, which is a...