Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models

Abstract

Spatial contiguity relationships represent a frequently ignored source of information that is available to economists modeling cross-sections of metropolitan areas, counties, states, regions, and countries. Shown here is how contiguity relationships can be incorporated as prior information in Bayesian vector autoregressive and error correction models with little or no effort, using existing software, to produce improvements in forecasting performance. A comparison of alternative forecasting methods is undertaken using annual postwar time series of agricultural output for a sample of 15 corn-producing states. The models that incorporate prior information regarding spatial contiguity are found to dominate those that ignore this information, producing much better forecasts.