This study introduces a novel Bayesian switching stochastic frontier model for predicting the direction of investment means, such as stocks, cryptocurrencies, and precious metals. The model considers three market states: bear, consolidation, and bull. We demonstrate the model's applicability...

During the COVID-19 pandemic, raising the hospital bed capacity was essential to ‘flattening the curve.’ However, due to short-run stickiness in hospital bed capacity, operational flexibility in managing the relative bed allocation for COVID-19 and non-COVID-19 patients was the key to...

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...

This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. With our new methodology, the original multivariate, n-dimensional model is treated as a set...

Existing methods for the measurement of technical efficiency in the dynamic production models obtain it from the implied distance functions without making use of the information about intertemporal economic behavior in the estimation beyond an indirect appeal to duality. The main limitation of...

We propose a dilution bias correction approach to deal with the errors-in-variables problem observed in realized volatility (RV) measures. The absolute difference between daily and monthly RV is shown to be proportional to the relative magnitude of the measurement error. Therefore, in...

We propose a bounds testing procedure (BTP) with a battery of tests for the existence of a non-degenerate co-integrating relationship in levels, for long panels. It is a natural extension to panel data of the respective approach in time series as described by Pesaran, Shin and Smith (2001) and...

The goal of this paper is to identify influential analysts who generate abnormal returns when issuing a new recommendation by building a model that bets on the sparsity of typical analysts' recommendation data. Based on Bayesian techniques, we estimate a regression model for the abnormal returns...

In this paper, we investigate whether COVID-19 has had an impact on household finances, like household debt repayments. To do so, the paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. We use data information related to...