Journal ArticleDOI
Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
TLDR
In this paper, asymptotic properties of the maximum likelihood estimators and the quasi-maximum likelihood estimator for the spatial autoregressive model were investigated. But the convergence rates of those estimators may depend on some general features of the spatial weights matrix of the model.Abstract:
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 those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with dif- ferent spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have >/n-rate of convergence and be asymp- totically normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.read more
Citations
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Journal ArticleDOI
Applied Spatial Econometrics: Raising the Bar
TL;DR: LeSage et al. as mentioned in this paper place the key issues and implications of the new ‘introductory’ book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial spillovers are significant, use of Bayesian posterior model probabilities to determine which spatial weights matrix best describes the data.
Journal ArticleDOI
Specification and Estimation of Spatial Panel Data Models
TL;DR: A survey of the specification and estimation of spatial panel data models can be found in this paper, where the authors discuss the asymptotic properties of the estimators and provide guidance with respect to the estimation procedures.
Posted Content
Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
TL;DR: This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations.
Journal ArticleDOI
Estimation of spatial autoregressive panel data models with fixed effects
Lung-fei Lee,Jihai Yu +1 more
TL;DR: This paper established asymptotic properties of quasi-maximum likelihood estimators for SAR panel data models with fixed effects and SAR disturbances and proposed an alternative estimation method based on transformation which yields consistent estimators with properly centered distributions.
References
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Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Book
Spatial Econometrics: Methods and Models
TL;DR: In this article, a typology of Spatial Econometric Models is presented, and the maximum likelihood approach to estimate and test Spatial Process Models is proposed, as well as alternative approaches to Inference in Spatial process models.