Several methods have been proposed in the last few years for evaluating uncertainty in forecasts produced by nonlinear econometric models. Some methods resort to Monte Carlo, while others resort to different simulation techniques. This work aims at comparing these methods by means of experiments...

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.

In econometric models specified as systems of simultaneous equations, forecast errors can be regarded as random variables whose variances can be investigated, analyzed and estimated. This book summarizes results available in the literature for linear and nonlinear econometric models, when...

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

The numerical example which completes the paper by Goldberqer, Nagar and Odeh, on the estimated asymptotic covariance matrix of the reduced form coefficients for the Klein-I model estimated by Two Stage Least Squares (2SLS), has led to some misinterpretations of the properties of the model. In...

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...

In nonlinear econometric models, the evaluation of forecast errors is usually performed, completely or partially, by resorting to stochastic simulation. However, for evaluating the specific contribution of errors in estimated structural coefficients, several alternative methods have been...