tailieunhanh - Handbook of Economic Forecasting part 56

Handbook of Economic Forecasting part 56. 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 | 524 . Stock and . Watson and Watson 2003 2004a Kitchen and Monaco 2003 and Aiolfi and Timmermann 2004 . The studies by Figlewski 1983 and Figlewski and Urich 1983 use static factor models for forecast combining they found that the factor model forecasts improved equal-weighted averages in one instance n 33 price forecasts but not in another n 20 money supply forecasts . Further discussion of these papers is deferred to Section 4. Stock and Watson 2003 2004b examined pooled forecasts of output growth and inflation based on panels of up to 43 predictors for each of the G7 countries where each forecast was based on an autoregressive distributed lag model with an individual Xt. They found that several combination methods consistently improved upon autoregressive forecasts as in the studies with small n simple combining methods performed well in some cases producing the lowest mean squared forecast error. Kitchen and Monaco 2003 summarize the real time forecasting system used at the . Treasury Department which forecasts the current quarter s value of GDP by combining ADL forecasts made using 30 monthly predictors where the combination weights depend on relative historical forecasting performance. They report substantial improvement over a benchmark AR model over the 1995-2003 sample period. Their system has the virtue of readily permitting within-quarter updating based on recently released data. Aiolfi and Timmermann 2004 consider time-varying combining weights which are nonlinear functions of the data. For example they allow for instability by recursively sorting forecasts into reliable and unreliable categories then computing combination forecasts with categories. Using the Stock-Watson 2003 data set they report some improvements over simple combination forecasts. 4. Dynamic factor models and principal components analysis Factor analysis and principal components analysis PCA are two longstanding methods for summarizing the main sources of variation and .