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Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
TLDR
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.Abstract:
One important goal of this study is to develop 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. We first generalize the generalized moments (GM) estimator suggested in Kelejian and Prucha (1998,1999) for the spatial autoregressive parameter in the disturbance process. We prove the consistency of our estimator; unlike in our earlier paper we also determine its asymptotic distribution, and discuss issues of efficiency. We then define instrumental variable (IV) estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GM estimator under reasonable conditions. Much of the theory is kept general to cover a wide range of settings. We note the estimation theory developed by Kelejian and Prucha (1998, 1999) for GM and IV estimators and by Lee (2004) for the quasi-maximum likelihood estimator under the assumption of homoskedastic innovations does not carry over to the case of heteroskedastic innovations. The paper also provides a critical discussion of the usual specification of the parameter space.read more
Citations
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Journal ArticleDOI
Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances
TL;DR: In this paper, 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 is developed.
Journal ArticleDOI
Estimating fully observed recursive mixed-process models with cmp 1
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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
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.
Book
Introduction to Statistical Time Series
TL;DR: In this paper, Fourier analysis is used to estimate the mean and autocorrelations of the Fourier spectral properties of a Fourier wavelet and the estimated spectrum of the wavelet.
Posted Content
R&D Spillovers and the Geography of Innovation and Production
TL;DR: In this paper, the spatial distribution of innovation activity and the geographic concentration of production are examined, using three sources of economic knowledge: industry R&D, skilled labor, and the size of the pool of basic science for a specific industry.
Journal ArticleDOI
Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
John C. Driscoll,Aart Kraay +1 more
TL;DR: The authors presented conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large.
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