The paper speaks of the technological evolution using a translog cost function for a western European country – Portugal. It begins by presenting the methodological framework, the estimation process based on the iterative Zellner method to estimate systems of seemingly unrelated regression...

Accurate estimation of the dominant root of a stationary but persistent time series are required to determine the speed at which economic time series, such as real exchange rates or interest rates, adjust towards their mean values. In practice, accuracy is hampered by downward small- sample...

Our study revisits Beck and Katz’ (1995) comparison of the Parks and PCSE estimators using time-series, cross-sectional data (TSCS). Our innovation is that we construct simulated statistical environments that are designed to closely match “real-world,” TSCS data. We pattern our statistical...

In this paper, we study the asymptotic distributions for least-squares (OLS), fully modified (FM), and dynamic OLS\ (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FM, and DOLS estimators are all asymptotically normally distributed. However, the...

We show in this paper that spatial dependence and spatial heterogeneity matter in the estimation of the b-convergence process among 138 European regions over the 1980-1995 period. Using spatial econometrics tools, we detect both spatial dependence and spatial heterogeneity in the form of...

This paper discusses two alternative two-part models for fractional response variables that are defined as ratios of integers. The first two-part model assumes a Binomial distribution and known group size. It nests the one-part fractional response model proposed by Papke and Wooldridge (1996)...

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence...

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that...

The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...