tailieunhanh - Handbook of Economic Forecasting part 62

Handbook of Economic Forecasting part 62. 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 | 584 G. Elliott various parameters on the value of including the cointegrating vector in the forecasting model controlled experiments will be difficult - changing a parameter involves a host of changes on the features of the model. In considering h step ahead forecasts we can recursively solve 10 to obtain yr h - yr XT h - xr y p- 1 1 -Q yM P1r h C 02 XT It 2t h 11 i i where u 1T h and ut h are unpredictable components. The result shows that the usefulness of the cointegrating vector for the h step ahead forecast depends on both the impact parameter a1 as well as the serial correlation in the cointegrating vector pc which is a function of the cointegrating vector as well as the impact parameter in both the equations. The larger the impact parameter all else held equal the greater the usefulness of the cointegrating vector term in constructing the forecast. The larger the root pc also the larger the impact of this term. These results give some insight as to the usefulness of the error correction term and show that different Monte Carlo specifications may well give conflicting results simply through examining models with differing impact parameters and serial correlation properties of the error correction term. Consider the differences between the results4 of Engle and Yoo 1987 and Christoffersen and Diebold 1998 . Both papers are making the point that the error correction term is only relevant for shorter horizons a point to which we will return. However Engle and Yoo 1987 claim that the error correction term is quite useful at moderate horizons whereas Christoffersen and Diebold 1998 suggest that it is only at very short horizons that the term is useful. In the former model the impact parameter is ay and pc . The impact parameter is of moderate size and so is the serial correlation and so we would expect some reasonable usefulness of the term for moderate horizons. In Christoffersen and Diebold 1998 these coefficients are ay -1 and pc 0. The large impact .