Sensitivity analysis of SAR estimators: a numerical approximation
The estimation of a spatial autoregressive (SAR) model depends on the spatial correlation parameter ρ in a highly nonlinear way, and the least squares (LS) estimators for ρ cannot be computed in a closed form. In this paper, we propose two simple LS estimators and compare them
by distance and covariance properties in order to study the local sensitivity behaviour of these estimators. Using matrix derivatives we calculate the Taylor approximation of the LS estimator in the SAR model up to the second order. In a next step, we compare the covariance structure of the
two estimators by Kantorovich inequalities and derive efficiency comparisons by upper bounds. Finally, we explore the quality of our new approximations by a Monte Carlo simulation study. The simulation results show significant computation time reductions and a good approximation behaviour
of the SAR LS estimator in the neighborhood of ρ=0, when using a non-spatial LS estimator. The results are encouraging and can be used for further developments like quick diagnostic tools to explore the sensitivity of spatial estimators with respect to the size of the spatial correlation.
Keywords: C11; C15; C52; E17; Kantorovich inequality; R12; Taylor approximations; least squares estimators; sensitivity analysis; spatial autoregressive models
Document Type: Research Article
Affiliations: 1: Faculty of information Sciences and Engineering,University of Canberra, Kirinari Street, Canberra,ACT 2601, Australia 2: Department of Economics and Finance,Institute for Advanced Studies, Stumpergasse 561060,Vienna, Austria
Publication date: 01 February 2012
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content