Research Database

This paper reports an optimistic forecast of U.S. output and inflation trends in 1990–91. Generated by a Bayesian vector autoregression (BVAR) model of the U.S. economy using data available on November 30, 1989, the forecast is more optimistic than a consensus forecast. The key to the model's greater optimism for real growth is its outlook for strong consumer spending. The model's optimism is defended by examining historical precedents as well as comparing the track records of the model and consensus forecasts. The model's measures of forecast uncertainty, however, suggest that its predictions should be taken cautiously. An appendix explains how the model can be used to generate conditional forecasts.

This article describes a new way to use monthly data to improve the national forecasts of quarterly economic models. This new method combines the forecasts of a monthly model with those of a quarterly model using weights that maximize forecasting accuracy. While none of the method's steps is new, it is the first method to include all of them. It is also the first method to be shown to improve quarterly model forecasts in a statistically significant way. And it is the first systematic forecasting method to be shown, statistically, to forecast as well as the popular survey of major economic forecasters published in the Blue Chip Economic Indicators newsletter. The method was designed for use with the quarterly model maintained in the Research Department of the Minneapolis Federal Reserve Bank, but can be tailored to fit other models. The Minneapolis Fed model is a Bayesian-restricted vector autoregression model.

This paper describes a method developed to predict the advance (first) estimate of inflation-adjusted gross national product (real GNP) using hours-worked data. Besides generating fairly accurate forecasts of advance GNP, the method has two implications. First, the Commerce Department seems to weigh the hours-worked data most heavily in its early estimates of real GNP but less and less so in its revised estimates. Second, analysts attempting to predict current-quarter outcomes in real time need to consider the availability and reliability of data at the time the forecasts are made.