A well known macroeconometric model of the Italian economy is updated to produce forecasts at 1974.

Experiments of stochastic simulation on a nonlinear macroeconometric model are described in this paper. The results are used both for improving the validation of a model of the Italian economy and for revisiting the heuristic value of the stochastic simulation methodology.

Experiments of stochastic simulation on a macro model of the Italian economy; this paper describes the first results produced by the research team.

Five alternative techniques have been applied to measure the degree of uncertainty associated with the forecasts produced by a macro-model of the French economy, the Mini-DMS developed at INSEE. They are bootstrap, analytic simulation on coefficients, Monte Carlo on coefficients, parametric...

Results of stochastic simulation experiments are described in this paper. The model experimented with is a large scale macroeconometric model, developed at the University of Bonn for the German economy (Model 5).

Through Monte Carlo experiments, this paper compares the performances of different gradient optimization algorithms, when performing full information maximum likelihood (FIML) estimation of econometric models. Different matrices are used (Hessian, outer products matrix, GLS-type matrix, as well...

Full information maximum likelihood estimation of econometric models, linear and nonlinear in variables, is performed by means of two gradient algorithms, using either the Hessian matrix or a computationally simpler approximation. In the first part of the paper, the behavior of the two methods...