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We evaluate the predictive content of Federal Reserve and Blue Chip forecasts of output growth by utilizing two comparable forecasts as benchmarks: a univariate autoregressive (AR) model, and a vector autoregressive (VAR) model which includes output growth, growth in residential investment, and consumers’ assessments of business conditions. We first show the forecasts are all directionally accurate, free of systematic bias, and efficient. Second, the asymmetric information hypothesis cannot be supported. Third, the Federal Reserve and private forecasts are generally less informative than the VAR forecasts and thus lack past information on residential investment growth and consumers’ assessments of business conditions.
5. Conclusions A good deal of literature concerns modeling and forecasting future growth in real output (Chauvet and Potter, 2013). In particular, decisions are guided by forecasts, and good forecasts are important in helping policymakers formulate successful economic policy. In this study, we compare the predictive content of both the Federal Reserve and Blue Chip forecasts of output growth using comparable AR and VAR forecasts. The univariate AR forecast contains past information in output growth, and the VAR forecast contains past information in output growth, growth in residential investment, and consumers’ assessments of business conditions known at the time of the forecast. We find that the VAR forecasts are more informative than the AR forecasts, indicating that growth in residential investment and consumers’ assessments of business conditions together are useful in predicting output growth.