Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop an unobserved-components approach to study surveys of forecasts containing multiple forecast horizons. Under the assumption that forecasters optimally update their beliefs about past, current, and future state variables as new information arrives, we use our model to extract information on the degree of predictability of the state variable and the importance of measurement errors in the observables. Empirical estimates of the model are obtained using survey forecasts of annual GDP growth and inflation in the United States with forecast horizons ranging from 1 to 24 months, and the model is found to closely match the joint realization of forecast errors at different horizons. Our empirical results suggest that professional forecasters face severe measurement error problems for GDP growth in real time, while this is much less of a problem for inflation. Moreover, inflation exhibits greater persistence, and thus is predictable at longer horizons, than GDP growth and the persistent component of both variables is well approximated by a low-order autoregressive specification.