We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it produces the same estimator as maximizing the...

This paper explores the properties of pre-test strategies in estimating a linear Cliff-Ord-type spatial model when the researcher is unsure about the nature of the spatial dependence. More specifically, the paper explores the finite sample properties of the pre-test estimators introduced in...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We develop two new estimators for a general class of stationary GARCH models with possibly heavy tailed asymmetrically distributed errors, covering processes with symmetric and asymmetric feedback like GARCH, Asymmetric GARCH, VGARCH and Quadratic GARCH. The first estimator arises from...

This work proposes a globally convergent algorithm, based on gradient projections, for maximum likelihood (ML) estimation under linear equality and inequality restrictions (constraints) on parameters. The proposed algorithm has wide applicability, and as an important special case its application...

The purpose of the present paper is to study in which cases the method of maximum likelihood is more preferential than other estimation methods of unknown distribution parameters, and also when it is necessary to use the Akaike or the RMSE criteria for selection of statistical models.The...

Easy to compute exact maximum likelihood estimators (MLEs) for parameters of a stochastic bivariate Itô Susceptible-Infected-Recovered (SIR) model and for parameters of an extension that treats undercounting are presented here

Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood...