Predictability of output growth and ination: A multi-horizon survey approach

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 in‡ation in the US with forecast horizons ranging from 1 to 24 months, and the model is found to closely match the joint realization of forecast errors at di¤erent 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 in‡ation. Moreover, in‡ation 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 speci…cation. Keywords: Fixed-event forecasts, multiple forecast horizons, Kalman …ltering, survey data. *We thank the editor, Serena Ng, an associate editor and two anonymous referees for constructive comments. We also thank seminar participants at the Board of Governors of the Federal Reserve, Cambridge University, City University London, Duke, European Central Bank, London School of Economics, NBER Summer Institute, ESAM08 meetings in Wellington, Oxford, Universite Libre Bruxelles (ECARES), Tilburg and Stanford (SITE workshop) for helpful comments. Timmermann acknowledges support from CREATES funded by the Danish National Research Foundation. Patton: Department of Economics, Duke University, 213 Social Sciences Building, Box 90097, Durham NC 27708-0097. Email: [email protected]. Timmermann: UCSD, 9500 Gilman Drive, La Jolla, CA 920930553. Email: [email protected].