Local to unity, long-horizon forecasting thresholds for model selection in the AR(1)

This article introduces a novel framework for analysing long-horizon forecasting of the near non-stationary AR(1) model. Using the local to unity specification of the autoregressive parameter, I derive the asymptotic distributions of long-horizon forecast errors both for the unrestricted AR(1), estimated using an ordinary least squares (OLS) regression, and for the random walk (RW). I then identify functions, relating local to unity ‘drift’ to forecast horizon, such that OLS and RW forecasts share the same expected square error. OLS forecasts are preferred on one side of these ‘forecasting thresholds’, while RW forecasts are preferred on the other. In addition to explaining the relative performance of forecasts from these two models, these thresholds prove useful in developing model selection criteria that help a forecaster reduce error.