The rapid spread of COVID-19 across the globe primed a variety of non-pharmaceutical interventions (NPIs). Given these NPIs, whether the SIR parameters followed a Bayesian learning, a random walk pattern or other type of learning with evolving epidemiological data over time has implications for...

We study loans from banking and non-banking lenders to different groups of borrowers in order to unveil significant differences on how those respond to a shock and evaluate possible alternative explanations for such differences. The objective is to gain insights useful to explain the loan...

We propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of systemic risk measures, LRMES, SRISK and ∆CoVaR, and apply it for thirty global systemically important banks and for eighteen largest US financial institutions over the period of 2000-2020. The advantage of the...

In this paper we propose a new small area estimation methodology aimed at the estimation of Value Added, Labor Cost and related competitiveness indicators for subsets of the population of Italian small and medium sized manufacturing firms classified according to geographical region, industrial...

In this paper, we consider the stochastic ray production function that has been revived recently by Henningsen et al. (2017). We use a profit-maximizing framework to resolve endogeneity problems that are likely to arise, as in all distance functions, and we derive the system of equations after...

The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm, introduced by Hoogerheide, Opschoor and Van Dijk (2012), provides an automatic and flexible method to approximate a non-elliptical target density using...

The tourism industry, in particular the hotel sector, is a highly competitive market. In this context, it is important that an hotel chain operates efficiently if it wants to improve or maintain its market position. The objective of this work is to compare the relative efficiency of hotel chains...