tailieunhanh - Handbook of Economic Forecasting part 52

Handbook of Economic Forecasting part 52. 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 | 484 H. White in each iteration of Step 1 replacing the search for the maximally correlated hidden unit term with a more extensive variable selection procedure based on CYMSE . By replacing CVMSE with AIC Cp GCV or other consistent methods for controlling model complexity one can easily generate other potentially appealing members of the QuickNet family as noted above. It is also of interest to consider the use of more recently developed methods for automated model building such as PcGets Hendry and Krolzig 2001 and RETINA Perez-Amaral Gallo and White 2003 2005 . Using either or both of these approaches in Step 1 results in methods that can select multiple hidden unit terms at each iteration of Step 1. In these members of the QuickNet family there is no need for Step 2 one simply iterates Step 1 until no further hidden unit terms are selected. Related to these QuickNet family members are methods that use multiple hypothesis testing to control the family-wise error rate FWER see Westfall and Young 1993 the false discovery rate FDR Benjamini 1995 and Williams 2003 the false discovery proportion FDP see Lehmann and Romano 2005 in selecting linear predictors in Step 0 and multiple hidden unit terms at each iteration of Step 1. In so doing care must be taken to use specification-robust standard errors such as those of Gonçalves and White 2005 . Again Step 2 is unnecessary the algorithm stops when no further hidden unit terms are selected. 6. Interpretational issues The third challenge identified above to the use of nonlinear forecasts is the apparent difficulty of interpreting the resulting forecasts. This is perhaps an issue not so much of difficulty but rather an issue more of familiarity. Linear models are familiar and comfortable to most practitioners whereas nonlinear models are less so. Practitioners may thus feel comfortable interpreting linear forecasts but somewhat adrift interpreting nonlinear forecasts. The comfort many practitioners feel with interpreting .