plm's Kelejian [4 articles]

Abstract

By far, the most popular test for spatial correlation is the one based on Moran's (1950) I test statistic. Despite this, the available results in the literature concerning the large sample distribution of this statistic are limited and have been derived under assumptions that do not cover many applications of interest. In this paper we first give a general result concerning the large sample distribution of Moran I type test statistics. We then apply this result to ...

Abstract

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 a computationally simple procedure for estimating cross-sectional models that contain both of these characteristics. We also give formal large-sample results. ...

Abstract

In this paper we consider a simultaneous system of spatially interrelated cross sectional equations. Our specification incorporates spatial lags in the endogenous and exogenous variables. In modelling the disturbance process we allow for both spatial correlation as well as correlation across equations. The data set is taken to be a single cross section of observations. The model may be viewed as an extension of the widely used single equation Cliff-Ord model. We suggest computationally simple limited and full information instrumental variable ...

Abstract

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 literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in this paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate- or large-sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. ...