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Indirect inference in spatial autoregression

Listed author(s):
  • Kyriacou, Maria
  • Phillips, Peter C.B.
  • Rossi, Francesca

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

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File URL: https://eprints.soton.ac.uk/374156/1/1418%2520combined.pdf
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Paper provided by Economics Division, School of Social Sciences, University of Southampton in its series Discussion Paper Series In Economics And Econometrics with number 1418.

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Date of creation: 22 Sep 2014
Handle: RePEc:stn:sotoec:1418
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