FIML estimates of a simultaneous equation econometric model can be obtained by iterating to convergence an instrumental variables formula that is perfectly consistent with the intuitive textbook-type interpretation of efficient instruments: instruments for an equation must be uncorrelated with...

The drawbacks of forecasts obtained with the usual deterministic solution methods in nonlinear systems of stochastic equations have been widely investigated in the literature. Most of the proposed therapies are based on some estimation of the conditional mean of the endogenous variables in the...

In econometric models, estimates of the asymptotic covariance matrix of FIML coefficients are traditionally computed in several different ways: with a generalized least squares type matrix; using the Hessian of the concentrated log-likelihood; using the outer product of the first derivatives of...

A method for evaluating the reliability of policy recommendations derived from a linear dynamic structural econometric model in the framework of the linear quadratic control problem has been recently proposed by Friedmann (1980, 1981). The method analytically derives the asymptotic distribution...

Some results os stochastic simulation of a small Italian macroeconometric model are presented.

DMS/2 (Decisional Models Solution, version 2) is a computer package for solution of nonlinear econometric models. This technical report describes the new features that improve over the DMS-package.

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...

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...

When econometric models are used as forecasting tools, forecast errors can be decomposed into several components, one of which is due to estimation errors, while another one is due to the stochastic nature of the variables to be predicted. Conditional on model's specification and on the...

Most of the methods proposed in the literature for evaluating forecast uncertainty in econometric models need an estimate of the structural coefficiencs covariance matrix among input data. When estimation is performed with full information maximum likelihood, alternative estimators of such a...