tailieunhanh - Handbook of Economic Forecasting part 65

Handbook of Economic Forecasting part 65. 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 | 614 . Clements and . Hendry are given in Clements and Hendry 1999 Chapter and are noted for convenience in Appendix A. This taxonomy conflates some of the distinctions in the general formulation above . mis-specification of deterministic terms other than intercepts and distinguishes others equilibrium-mean and slope estimation effects . Thus the model mis-specification terms iia and iib may result from unmodeled in-sample structural change as in the general taxonomy but may also arise from the omission of relevant variables or the imposition of invalid restrictions. In 10 terms involving yT y have zero expectations even under changed parameters . ib and iib . Moreover for symmetrically-distributed shocks biases in n for n will not induce biased forecasts see . Malinvaud 1970 Fuller and Hasza 1980 Hoque Magnus and Pesaran 1988 and Clements and Hendry 1998 for related results . The eT h have zero means by construction. Consequently the primary sources of systematic forecast failure are ia iia iii and iva . However on ex post evaluation iii will be removed and in congruent models with freely-estimated intercepts and correctly modeled in-sample breaks iia and iva will be zero on average. That leaves changes to the equilibrium mean y not necessarily the intercept 0 in a model as seen in 10 as the primary source of systematic forecast error see Hendry 2000 for a detailed analysis. 3. Breaks in variance . Conditional variance processes The autoregressive conditional heteroskedasticity ARCH model of Engle 1982 and its generalizations are commonly used to model time-varying conditional processes see inter alia Engle and Bollerslev 1987 Bollerslev Chou and Kroner 1992 and Shephard 1996 and Bera and Higgins 1993 and Baillie and Bollerslev 1992 on forecasting. The forecast-error taxonomy construct canbe applied to variance processes. We show that ARCH and GARCH models can in general be solved for long-run variances so like VARs are a member of the .