TAILIEUCHUNG - Handbook of Economic Forecasting part 50

Handbook of Economic Forecasting part 50. 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 | 464 H. White e y - ii xt 2 E jj. xt - X tp 2 . The final equality follows from the fact that for all p E Yt - p Xt p Xt - X tp E E Y - fi Xtp fi Xt - X tp Xt E E Yt - X Xt M Xt - X tp 0 because E Yt - p Xt Xt 0. Thus E Yt - X tp 2 E Yt - Xt 2 E Xt - Xftp 2 a f x - x p 2dH x 3 where dH denotes the joint density of Xt and a denotes the pure PMSE a E Yt - Xt 2 . From 3 we see that the PMSE can be decomposed into two components the pure PMSE a 2 associated with the best possible prediction that based on and the approximation mean squared error AMSE p. x - xp 2 dH x for xp as an approximation to p x . The AMSE is weighted by dH the joint density of Xt so that the squared approximation error is more heavily weighted in regions where Xt is likely to be observed and less heavily weighted in areas where Xt is less likely to be observed. This weighting forces the optimal approximation to be better in more frequently observed regions of the distribution of Xt at the cost of being less accurate in less frequently observed regions of the distribution of Xt. It follows that to minimize PMSE it is necessary and sufficient to minimize AMSE. That is because p minimizes PMSE it also satisfies p arg min f p x - x p 2 dH x . peRk This shows that p is the vector delivering the best possible approximation of the form x p to the PMSE-best predictor p x of Yt given Xt x where the approximation is best in the sense of AMSE. For brevity we refer to this as the optimal approximation property . Note that AMSE is nonnegative. It is minimized at zero if and only if for some po p x x po that is if and only if L is correctly specified. In this case p po. An especially convenient property of p is that it can be represented in closed form. The first order conditions for p from problem 2 can be written as E XtX t p - E XtYt 0. Define M E XtX t and L E XtYt . If M is nonsingular then we can solve for p to obtain the desired closed form expression p M-1L. Ch. 9 Approximate Nonlinear Forecasting

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