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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 unrestriczed 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.

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This study extends the work of Estrella and Mishkin (1996, 1998) to show that interest-rate spreads and probit modeling can be used to predict recessions in many states as well as the nation. State recessions are defined as two or more consecutive quarters of declining real gross state product. The yield spread, "SPREAD", is defined as the difference between the 10-year Treasury bond rate and the three-month Treasury bill rate. The national results are similar to those obtained by Estrella and Mishkin. Probit models are estimated for all 50 states using SPREAD and unemployment insurance claims, "UI", as alternative explanatory variables. For 34 of the 50 states, SPREAD is significant at the 0.01 level as a predictor of state recessions. Much weaker results are obtained using "UI". Simulations for the 1979-2001 period are used to compute loss functions for the national and state models at probability screens of 30, 40, 50, and 60 percent. The results demonstrate that probit models based on "SPREAD" can be useful in improving business and policy decisions in many states. Copyright Blackwell Publishing, Inc 2003