This article provides a semiparametric method for the estimation of truncated regression models where the disturbances are independent of the regressors before truncation. This independence property provides useful information on model identification and estimation. Our estimate is shown to be...

This article considers methods of simulated moments for estimation of discrete response models. It is possible to use the same set of random numbers to simulate the choice probabilities for each individual in the sample. In addition to the method of simulated moments of McFadden, we have...

This paper examines the asymptotics of the QMLE for unit root dynamic panel data models with spatial effect and fixed effects. We consider a unit root dynamic panel data model with spatially correlated disturbances and a unit root spatial dynamic panel data model. For both models the estimate of...

This paper establishes asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with both time and individual fixed effects when the number of individuals <italic>n</italic> and the number of time periods <italic>T</italic> can be large. We propose a data transformation approach to eliminate...

In this paper, we extend the GMM framework for the estimation of the mixed-regressive spatial autoregressive model by Lee(2007a) to estimate a high order mixed-regressive spatial autoregressive model with spatial autoregressive disturbances. Identification of such a general model is considered....

This paper considers the extension of the classical minimum distance approach for the pooling of estimates with various rates of convergence. Under a setting where relatively high rates of convergence can be attained, the minimum distance estimators are shown to be consistent and asymptotically...

In this article, we investigate a bias in an asymptotic expansion of the simulated maximum likelihood estimator introduced by Lerman and Manski (pp. 305–319 in C. Manski and D. McFadden (eds.), <italic>Structural Analysis of Discrete Data with Econometric Applications</italic>, Cambridge: MIT Press, 1981) for...