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Assessment of poverty has generally been carried out using “money-metric” measures. But since poverty is multidimensional, these measures fall short of generating a comprehensive picture of the poor. ...
The College of Economics and Trade, Hunan University, together with International Regional Science Review, Journal of Regional Science, Spatial Economic Analysis, Growth and Change, Journal of Social ...
The Wang Yanan Institute for Studies in Economics (WISE) and the School of Economics (SOE), Xiamen University, together with Spatial Economic Analysis, Journal of Regional Science, Papers in Regional ...
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 computati...
The purpose of this paper is two-fold. First, on a theoretical level we introduce a series-type instrumental variable (IV) estimator of the parameters of a spatial first order autoregressive model wit...
Spatial models whose weighting matrices have blocks of equal elements might be considered if units are viewed as equally distant within certain neighborhoods, but unrelated between neighborhoods. We g...
We suggest a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance–covariance (VC) matrix for a vector of sample moments within a spatial context. Wedemonstr...
In this paper, we specify a linear Cliff-and-Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the dist...
The purpose of this paper is to suggest estimators for the parameters of spatial models containing a spatially lagged dependent variable, as well as spatially lagged independent variables, and an inco...
The purpose of this paper is to describe prediction efficiencies of various suboptimal predictors relative to the efficient (kriging) minimum mean square error predictor in spatial models containing s...
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 a...
The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investig...
The paper considers a Cliff–Ord type spatial model with a spatially lagged dependentvariable and a row normalized weighting matrix with equal weights. We show that the 2SLSand OLS estimators are incon...
The development of a general inferential theory for nonlinear models with cross-sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems. To facilitate a gener...
We examine spatial spillovers in institutional development. Dependent variables are institutional measures reflecting politics, law, and governmental administration. The explanatory variable of intere...

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