On the Finite Sample Properties of Pre-test Estimators of Spatial Models

This paper explores the properties of pre-tst strategies in estimating a linear Cliff-Ord -type spatial model when the researcher is unsure about the nature of the spatial dependence. More specifically, the paper explores the finite sample properties of the pre-test estimators introduced in Florax et al. (2003), which are based on Lagrange Multiplier (LM) tests, within the context of a Monte Carlo study. The performance of those estimators is compared with that of the maximum likelihood (ML) estimator of the encompassing model. We find that, even in a very simple setting, the bias of the estimates generated by pre-testing strategies can be very large in some cases and the empirical size of tests can differ substantially from the nominal size. This is in contrast to the ML estimator.