A Joint Bayesian Forecasting Model of Judgment and Observed Data

This paper presents a new approach that aims to incorporate prior judgmental forecasts into a statistical forecasting model. The result is a set of forecasts that are consistent with both the judgment and latest observations. The approach is based on constructing a model with a combined dataset where the expert forecasts and the historical data are described by means of corresponding regression equations. Model estimation is done using numeric Bayesian analysis. Semiparametric methods are used to ensure finding adequate forecasts without any prior knowledge of the specific type of the trend function. The expert forecasts can be provided as estimates of future time series values or as estimates of total or average values over any particular time intervals. Empirical analysis has shown that the approach is operable in practical settings. Compared to standard methods of combining, the approach is more flexible and in empirical comparisons proves to be more accurate.