Inference on Higher-Order Spatial Autoregressive Models with Increasingly Many Parameters

This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squares and instrumental variables estimates are examined, and the permissible rate of growth of the dimension of the parameter space relative to sample size is studied. Besides allowing the number of estimable parameters to increase with the data, this has the advantage of accommodating explicitly some asymptotic regimes that arise in practice. Illustrations are discussed, in particular settings where the need for such theory arises quite naturally. A Monte Carlo study analyses various implications of the theory in finite samples. For empirical researchers our work has implications for the choice of model. In particular if the structure of the spatial weights matrix assumes a partitioning of the data then spatial parameters should be allowed to vary over clusters.