TAILIEUCHUNG - Handbook of Economic Forecasting part 6

Handbook of Economic Forecasting part 6. 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 | 24 J. Geweke and C. Whiteman requirement is equivalent to a record of the one-step-ahead predictive likelihoods p yot Yot-1 Aj t 1 . T for each model. It is therefore not surprising that most of the prediction work based on model combination has been undertaken using models also designed by the combiners. The feasibility of this approach was demonstrated by Zellner and coauthors Palm and Zellner 1992 Min and Zellner 1993 using purely analytical methods. Petridis et al. 2001 provide a successful forecasting application utilizing a combination of heterogeneous data and Bayesian model averaging. . Conditional forecasting In some circumstances selected elements of the vector of future values of y may be known making the problem one of conditional forecasting. That is restricting attention to the vector of interest a yT 1 . yT F one may wish to draw inferences regarding a treating S1y T 1 . SFy T F Sa as known for q x p selection matrices S1 . SF which could select elements or linear combinations of elements of future values. The simplest such situation arises when one or more of the elements of y become known before the others perhaps because of staggered data releases. More generally it may be desirable to make forecasts of some elements of y given views that others follow particular time paths as a way of summarizing features of the joint predictive distribution for yT 1 . yT F . In this case focusing on a single model A 25 becomes p a Sa YT A p 0a Sa Y A p a Sa Y 0a d0a. 32 0A As noted by Waggoner and Zha 1999 this expression makes clear that the conditional predictive density derives from the joint density of 0 A and a. Thus it is not sufficient for example merely to know the conditional predictive density p a YoT 0 A because the pattern of evolution of yT 1 . yT F carries information about which 0 A are likely and vice versa. Prior to the advent of fast posterior simulators Doan Litterman and Sims 1984 produced a type of conditional forecast from a Gaussian .

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