As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of...

Cet article présente les contributions originelles et essentielles de T. Sargent et C. Sims à la modélisation macro-économétrique. Après avoir exposé leur critique de la modélisation existante, cet article s'attache à préciser l'originalité de leurs approches respectives. La...

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)...

Cet article présente les contributions originelles et essentielles de T. Sargent et C. Sims à la modélisation macro-économétrique. Après avoir exposé leur critique de la modélisation existante, cet article s'attache à préciser l'originalité de leurs approches respectives. La...

Multilevel Analysis - Techniques and Applications is intended as an introduction to multilevel analysis for students and applied researchers, a book that can be used either in psychology, education, sociology, or business courses. Hox takes the reader step by step into the area of multilevel...

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...

It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that...