This paper explores sample size requirements for the estimation of SUR models by (two-stage) feasible generalized least squares, maximum likelihood and Bayesian methods. It is found that the sample size requirements presented in standard treatments of SUR models are incomplete and potentially...

We focus on the concept of impulse in econometrics analysis of time series. We recognize two useful characterizations, which are respectively deterministic and stochastic.

This paper suveys several recently proposed regression-based methods that can be used to evaluate the usefulness of seasonally adjusted data, with specific focus on Census X-12 adjustment. These methods examine whether seasonality has indeed been removed and whether key properties (like a trend...

We describe in this paper a method allowing to order submodels in linear regression. A real function is attached to each submodel, allowing to graphically compare and order them. Our procedure defines an objective function depending on two factors (lack of fit and multicolinearity) with the...

In this paper we develop a general strategy for studying the effect on unbiased, nearest-neighbor walks of opening up or blocking a trap or neural site on a d-dimensional lattice.

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...

Adopting a system approach, the paper evaluates results of empirical research on money demand recently obtained at the Bank of Italy in a single equation context.

We propose in this paper a Bayesian approach with a noninformative prior distribution developed in Mengersen and Robert (1996) and Robert and Titterington (1996) in the setup of mixtures of distributions and hidden Markov models, respectively.

The estimation of the noncentrality parameter of a chi-squared distribution, although simple to state, leads to difficulties, both for frequentist and Bayesian inferences. We propose in this paper a family of admissible improper Bayes estimators and study the minimax behavior of these estimators...

We study a panel structure with n subjects/entities being observed over T periods. We consider a class of models for each subject's data generating precess, and allow the unknown heterogeneity. In other words, we do not know many types we have, what the types are, and which subjects belong to...