Corpus ID: 221593822

Maximum Likelihood Estimation of a Spatial Autoregressive

@inproceedings{Xu2013MaximumLE,
  title={Maximum Likelihood Estimation of a Spatial Autoregressive},
  author={X. Xu},
  year={2013}
}
This paper examines a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence of the dependent variable process generated from the model structure. We show that the maximum likelihood estimator is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator. JEL… Expand

Tables from this paper

Tobit models with social interactions: Complete vs incomplete information
In many network data sets, the outcomes of interest are equal to zero for some agents and strictly positive for others. They can be analyzed by Tobit models with social interactions with differentExpand
Essays in economics of education and econometric theory
This doctoral thesis is composed of three chapters on economics of education and econometric theory. Chapter 2 studies how students interact in teams and gives some guidance to educators how to groupExpand

References

SHOWING 1-10 OF 38 REFERENCES
A spatial autoregressive model with a nonlinear transformation of the dependent variable
This paper develops a nonlinear spatial autoregressive model. Of particular interest is a structural interaction model for share data. We consider possible instrumental variable (IV) and maximumExpand
A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literatureExpand
Bayesian Estimation of Limited Dependent Variable Spatial Autoregressive Models
A Gibbs sampling (Markov chain Monte Carlo) method for estimating spatial autoregressive limited dependent variable models is presented. The method can accommodate data sets containing spatialExpand
Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of thoseExpand
A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances
Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe aExpand
Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models
This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposingExpand
GENERALIZED MAXIMUM ENTROPY ESTIMATION OF A FIRST ORDER SPATIAL AUTOREGRESSIVE MODEL
We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. MonteExpand
Estimating a spatial autoregressive model with an endogenous spatial weight matrix
The spatial autoregressive (SAR) model is a standard tool for analyzing data with spatial correlation. Conventional estimation methods rely on the key assumption that the spatial weight matrix isExpand
GMM and 2SLS estimation of mixed regressive, spatial autoregressive models
The GMM method and the classical 2SLS method are considered for the estimation of mixed regressive, spatial autoregressive models. These methods have computational advantage over the conventionalExpand
Estimation of Sample Selection Models with Spatial Dependence
We consider the estimation of sample selection (type II Tobit) models that exhibit spatial error dependence or spatial autoregressive errors (SAE). The method considered is motivated by a two-stepExpand
...
1
2
3
4
...