Does Disagreement amongst Forecasters have Predictive Value?

Forecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst forecasters. The premise is that this variable could signal upcoming structural or temporal changes in an economic process or in the predictive power of the survey forecasts. In our empirical work, we examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime-switching models with constant and with time-varying transition probabilities are constructed in real-time and compared on forecast accuracy. We find that disagreement has predictive power indeed and that this variable can be used to improve forecasts when used in Markov regime-switching models.