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...

The autoregressive conditional heteroskedasticity (ARCH) estimation procedure provides a specification of the error terms as well as estimates of the coefficients. A simple interest rate equation is estimated using least squares and also using ARCH. Then the stochastic simulation methodology is...

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

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...

Numerical simulation methods can overcome the difficulties and limitations of analytical methods, when analyzing dynamic properties of econometric models.

In this paper the results of six different estimation methods appliead to a linear aggregated model of the Italian economy are at first displayed. Afterwards, the inherent dynamic characteristics and the simulation properties of the six sets of estimates are analyzed. In no case the obtained...