This paper uses Data Envelopment Analysis (DEA) model formulations in order to determine the performance levels of 16 departments of the University of Thessaly. Particularly, the constant returns to scale (CRS) and variable returns to scale (VRS) models have been applied alongside with bootstrap...

The paper develops empirical implementations of the standard time-varying Panel Bayesian VAR model to deal with confounding and latent effects. Bayesian computations and mixed hierarchical distributions are used to generate posteriors of conditional impulse responses and conditional forecasts....

Futures contracts on the New York Mercantile Exchange are the most liquid instruments for trading crude oil, which is the world’s most actively traded physical commodity. Under normal market conditions, traders can easily find counterparties for their trades, resulting in an efficient market...

This paper studies identification of linear rational expectations models under news shocks. Exploiting the general martingale difference solution approach, we show that news shocks models are observationally equivalent to a class of indeterminate equilibrium frameworks which are subject only,...

This note points out a hitherto unrecognised identification issue in a class of rational expectations (RE) models with news shocks. We show that different degrees of anticipation (information flows) have strikingly different implications for the identifiability of the underlying structural...

Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of...

This paper constructs the probability space underlying the random variable of any time dependent econometric specification. The construction links concrete economic activity, both perceived and recorded, and econometric formulations. Furthermore, it is argued that the probability events...

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into...

Reliability has been largely applied to industrial systems in order to study the various possibilities of systems’ failure. The goal is to establish the chain of events leading to any system’s failure, namely the top event. Looking for the minimal paths leading to any system’s fault allows...

Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative...