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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...
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volatility specifications commonly adopted in the literature. Within this framework, we show that the standard heteroskedasticity-autocorrelation …
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The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies …
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This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial...
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