Autoregressive Spatial Spectral Estimates

Autoregressive spectral density estimation for stationary random fields on a regular spatial lattice has many advantages relative to kernel based methods. It provides a guaranteed positive-definite estimate even when suitable edge-effect correction is employed, is simple to compute using least squares and necessitates no choice of kernel. We truncate a true half-plane infinite autoregressive representation to estimate the spectral density. The truncation length is allowed to diverge in all dimensions in order to avoid the potential bias which would accrue due to truncation at a fixed lag-length. Consistency and strong consistency of the proposed estimator, both uniform in frequencies, are established. Under suitable conditions the asymptotic distribution of the estimate is shown to be zero-mean normal and independent at fixed distinct frequencies, mirroring the behaviour for time series. A small Monte Carlo experiment examines finite sample performance. We illustrate the technique by applying it to Los Angeles house price data and a novel analysis of voter turnout data in a US presidential election. Technically the key to the results is the covariance structure of stationary random fields defined on regularly spaced lattices. We study this in detail and show the covariance matrix to satisfy a generalization of the Toeplitz property familiar from time series analysis.