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Indirect inference in spatial autoregression

Maria Kyriacou (), Peter Phillips () and Francesca Rossi ()

Discussion Paper Series In Economics And Econometrics from University of Southampton, Economics Division, School of Social Sciences

Abstract: Ordinary least squares (OLS) is well-known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator is robust to departures from normal disturbances and is computationally straightforward compared with pseudo Gaussian maximum likelihood (PML). Monte Carlo experiments based on various specifications of the weighting matrix confirm that the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the Gaussian PML. Keywords; bias, binding function, inconsistency, indirect inference, spatial autoregression

New Economics Papers: this item is included in nep-ecm and nep-ure
Date: 2014-09-22
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Journal Article: Indirect inference in spatial autoregression (2017) Downloads
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