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...

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

Some analytic simulation techniques for the analysis of the reduced form and of the dynamic properties of econometric models are described. Comparisons are made with analytical methods available for linear models.

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...

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...

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...

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.

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...