We conduct a Monte Carlo study of the global regularity properties of the Normalized Quadratic model. We particularly investigate monotonicity violations, as well as the performance of methods of locally and globally imposing curvature. We find that monotonicity violations are especially likely...

The purpose of this paper was to apply the econometric models with qualitative variables in order to analyze two non academic behaviors at the level of the Romanian higher education system: cheating on the exams by copying or by direct or intermediary intervention at the professor.

The hedge fund represents a unique investment opportunity for the institutional and private investors in the diffusion-type financial systems. The main objective of this condensed article is to research the hedge fund’s optimal investment portfolio strategies selection in the global capital...

An approach to incorporate spatial dependence into Stochastic Frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial...

This paper redefines technical efficiency by incorporating provision of environmental goods as one of the outputs of the farm within a multi-outptut distance function framework. Permanent and rough grassland area are used as a proxy for the provision of environmental goods. The multi-output...

In this paper, new estimating methods proposed for dynamic and static probit models with panel data. Simulation studies show that the proposed estimators work relatively well.

We introduce and implement a reversible jump approach to Bayesian Model Averaging for the Probit model with uncertain regressors. This approach provides a direct estimate of the probability that a variable should be included in the model. Two applications are investigated. The �rst is the...

Over the last two decades, alternative expected return proxies have been proposed with substantially lower variation than realized returns. This helped to reduce parameter uncertainty and to identify many seemingly robust relations between expected returns and variables of interest, which would...

This paper proposes a new model averaging estimator for the linear regression model with heteroskedastic errors. We address the issues of how to optimally assign the weights for candidate models and how to make inference based on the averaging estimator. We derive the asymptotic mean squared...

This paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework. We show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug-in averaging estimator that minimizes the...