Improved asymptotic analysis of Gaussian QML estimators in spatial models

Jakub Olejnik and Alicja Olejnik ()
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Jakub Olejnik: Department of Mathematics and Computer Science, University of Lodz

No 9/2017, Lodz Economics Working Papers from University of Lodz, Faculty of Economics and Sociology

Abstract: This paper presents a fundamentally improved statement on asymptotic behaviour of the well-known Gaussian QML estimator of parameters in high-order mixed regressive/autoregressive spatial model. We generalize the approach previously known in the econometric literature by considerably weakening assumptions on the spatial weight matrix, distribution of the residuals and the parameter space for the spatial autoregressive parameter. As an example application of our new asymptotic analysis we also give a statement on the large sample behaviour of a general fixed effects design.

Keywords: spatial autoregression; quasi-maximum likelihood estimation; high-order SAR model; asymptotic analysis; fixed effects model (search for similar items in EconPapers)
JEL-codes: C21 C23 C51 (search for similar items in EconPapers)
Date: 2017-12
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-ure
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