QML Estimation of the Spatial Weight Matrix in the MR-SAR Model

Saruta Benjanuvatra and Peter Burridge

Discussion Papers from Department of Economics, University of York

Abstract: We investigate QML estimation of a parametric form for the spatial weight matrix, W, appearing in the mixed regressive, spatial autoregressive (MR-SAR) model and extend the identifiability, consistency, and asymptotic Normality results given by Lee (2004, 2007) to the case when W depends on an unknown parameter, y, that is to be estimated from a single cross-section. Numerical experiments illustrate that the QML estimator works quite well inmoderate sized samples, yielding well-behaved parameter estimates and t-statistics with approximately correct size in most cases. These findings should open the door to a much more flexible approach to the construction of spatial regression models. Finally, the QML estimator using two types of sub-models for the spatial weights is applied to the cross-sectional dataset used in Ertur and Koch (2007), to illustrate the utility of the approach.

Keywords: Spatial autoregressive model; estimated spatial weight matrix; quasi-maximum likelihood estimator; growth spillovers. (search for similar items in EconPapers)
JEL-codes: C13 C15 C21 R15 (search for similar items in EconPapers)
Date: 2015-09
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:15/24

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