Add to ORKGFull 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 in getting the optimum is investigated with Monte Carlo experimentation on some models of small and medium size. In the second part of the paper, the behavior of the two matrices in producing estimates of the asymptotic covariance matrix of coefficients is analyzed and, again. experimented with Monte Carlo on the same models. Some systematic differences are evidenced
With most of the available software packages, estimates of the parameter covariance matrix in a GARC...
Abstract: Maximum likelihood (ML) estimation using Newton’s method in nonlinear state space models (...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be ...
Full information maximum likelihood estimation of econometric models, linear and nonlinear in variab...
Through Monte Carlo experiments, this paper compares the performances of different gradient optimiza...
AbstractThe computation of statistical properties in nonlinear parameter estimation is generally car...
The Hessian of the multivariate normal mixture model is derived, and estimators of the information m...
Most of the methods proposed in the literature for evaluating forecast uncertainty in econometric mo...
AbstractThis paper develops two algorithms. Algorithm 1 computes the exact, Gaussian, log-likelihood...
Econometric techniques to estimate output supply systems, factor demand systems and consumer demand ...
In econometric models, estimates of the asymptotic covariance matrix of FIML coefficients are tradit...
In Bayesian system identification with globally identifiable models, the posterior (i.e., given data...
When the coefficients of a Tobit model are estimated by maximum likelihood their covariance matrix i...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
This paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIM...
With most of the available software packages, estimates of the parameter covariance matrix in a GARC...
Abstract: Maximum likelihood (ML) estimation using Newton’s method in nonlinear state space models (...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be ...
Full information maximum likelihood estimation of econometric models, linear and nonlinear in variab...
Through Monte Carlo experiments, this paper compares the performances of different gradient optimiza...
AbstractThe computation of statistical properties in nonlinear parameter estimation is generally car...
The Hessian of the multivariate normal mixture model is derived, and estimators of the information m...
Most of the methods proposed in the literature for evaluating forecast uncertainty in econometric mo...
AbstractThis paper develops two algorithms. Algorithm 1 computes the exact, Gaussian, log-likelihood...
Econometric techniques to estimate output supply systems, factor demand systems and consumer demand ...
In econometric models, estimates of the asymptotic covariance matrix of FIML coefficients are tradit...
In Bayesian system identification with globally identifiable models, the posterior (i.e., given data...
When the coefficients of a Tobit model are estimated by maximum likelihood their covariance matrix i...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
This paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIM...
With most of the available software packages, estimates of the parameter covariance matrix in a GARC...
Abstract: Maximum likelihood (ML) estimation using Newton’s method in nonlinear state space models (...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be ...