We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator of the parameters in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. We introduce a new saddlepoint density and tail area approximation to...

We introduce Indirect Robust Generalized Method of Moments (IRGMM), a new simulation-based estimation methodology, to model short-term interest rate processes. The primary advantage of IRGMM relative to classical estimators of the continuous-time short-rate diffusion processes is that it...

Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modeling of relationships between manifest and latent variables. They extend structural equation modeling techniques, which are powerful tools in the social sciences. However, because of the...

Generalized Linear Latent Variables Models (GLLVM) enable the modeling of relationships between manifest and latent variables, where the manifest variables are distributed according to a distribution of the exponential family (e.g. binomial or normal) and to the multinomial distribution (for...

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these...

This paper presents a class of robust estimators for linear and non-linear simultaneous equations models, which are a direct generalization of the maximum likelihood estimator. The new estimators are obtained as solutions of a generalized likelihood equation. They are resistant to deviations...