• Co-integration;
  • Error correction;
  • Regional forecasting;
  • Vector autoregression;
  • Bayesian vector autoregression


This study investigates possible improvements in medium-term VAR forecasting of state retail sales and personal income when the two series are co-integrated and represent an error-correction system. For each of North Carolina and New York, three regional vector autoregression (VAR) models are specified; an unrestricted two-equation model consisting of the two state variables, a five-equation unrestricted model with three national variables added and a Bayesian (BVAR) version of the second model. For each state, the co-integration and error-correction relationship of the two state variables is verified and an error-correction version of each model specified. Twelve successive ex ante five-year forecasts are then generated for each of the state models. The results show that including an error-correction mechanism when statistically significant improves medium-term forecasting accuracy in every case.