Prediction from the Dynamic Simultaneous Equation Model with Vector Autoregressive Errors

Abstract

The asymptotic distribution of prediction is derived for the general simultaneous equation model with lagged endogenous variables and vector autoregressive errors. The results turn out to be particularly simple when no lagged endogenous variables are present. errors. It seems worthwhile to extend Schmidt's results, since the estimation of dynamic simultaneous equation models with autoregressive errors is now fairly commonplace in the literature. Also, as noted by Schmidt (5), the effects of parameter estimation can produce a significant effect on forecast confidence intervals. In fact, the results we obtain are computationally feasible making it possible to calculate the term due to parameter estimation in a forecast confi- dence interval. For the seemingly unrelated regression model with vector autore- gressive errors, but with no lagged dependent variables, the results turn out to be particularly straightforward generalizations of those obtained by Baillie (1), who