Maximum Likelihood Estimation of Endogenous Switching Regression Models

Estimation of Markov Regime-Switching Regression Models with Endogenous Switching

Following Hamilton (1989), estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive fil...

Lecture 1: Maximum likelihood estimation of spatial regression models

Maximum likelihood estimation in semiparametric regression models with censored data

Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present ...

A comparison of algorithms for maximum likelihood estimation of Spatial GLM models

In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...

Modified Maximum Likelihood Estimation in Poisson Regression

In Generalized Linear Models, likelihood equations are intractable and do not have explicit solutions; thus, they must be solved by using Newton-type algorithms. Solving these equations by iterations, however, can be problematic: the iterations might converge to wrong values or the iterations might not converge at all. In this study, we derive the modified maximum likelihood estimators for Pois...