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

For some structural econometric models, the contribution of the off-diagonal blocks of the coefficients covariance matrix to the asymptotic standard errors of multipliers and forecasts is empirically evaluated.

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.

This paper presents an interactive computer package to handle and process economic data organized as time series. It is expected to provide help and great benefits to econometric model builders.

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...

When dealing with nonlinear econometric models, resort is often made to simulation techniques for the investigation of their dynamic properties. A spectral analysis using stochastic and analytic simulation is carried out on a nonlinear model of the Italian economy. The two approaces are...

Markov Switching models have been successfully applied to many economic problems. The most popular version of these models implies that the change in the state is driven by a Markov Chain and that the state is an exogenous discrete unobserved variable. This hypothesis seems to be too...

We consider a simultaneous equation model with two endogenous limited dependent variables (individual wage and reservation wage) characterized by a selection mechanism determining a two-regimes endogenous-switching. We extend the FIML procedure proposed by Poirier-Ruud (1981) for a single...

The estimation of limited dependent variable panel data models usually involves objective functions in which integrals appear without a closed form solution: this is the case of the panel data Tobit model with random effects. Recently, simulation methods have shown to be useful in the inference...

For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assumptions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate normal linear regression. Disentangling the...