Handbook of Economic Forecasting part 12. Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing | 84 . Granger and MJ. Machina 1997 . Early in their book on page 14 West and Harrison state A statistician economist or management scientist usually looks at a decision as comprising a forecast or belief and a utility or reward function . Denote Y as the outcome of a future random quantity which is conditional on your decision a expressed through a forward or probability function P Y a . A reward function u Y a expresses your gain or loss if Y happens when you take decision a . In such a case the expected reward is r a I u Y a dP Y a 1 and the optimal decision is taken to be the one that maximizes this expected reward. The parallel with the expected utility literature is clear. The book continues by discussing a dynamic linear model denoted DLM using a state-space formulation. There are clear similarities with the Kalman filtering approach but the development is quite different. Although West and Harrison continue to develop the Bayesian maximum reward approach according to their index the words decision and utility are only used on page 14 as mentioned above. Although certainly important in Bayesian circles it was less influential elsewhere. This also holds for the large body of work known as statistical decision theory which is largely Bayesian. The later years of the Twentieth Century produced a flurry of work published around the year 2000. Chamberlain 2000 was concerned with the general topic of econometrics and decision theory - in particular with the question of how econometrics can influence decisions under uncertainty - which leads to considerations of distributional forecasts or predictive distributions . Naturally one needs a criterion to evaluate procedures for constructing predictive distributions and Chamberlain chose to use risk robustness and to minimize regret risk. To construct predictive distributions Bayes methods were used based on parametric models. One application considered an individual trying to forecast their future earnings using .
