tailieunhanh - Handbook of Economic Forecasting part 101

Handbook of Economic Forecasting part 101. 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 | 974 D. Croushore results. When model developers using latest-available data find lower forecast errors than real-time forecasters did it may not mean that their forecasting model is superior it might only mean that their data are superior because of the passage of time. Experiment 3 Information criteria and forecasts In one final set of experiments Stark and Croushore look at the choice of lag length in an ARIMA p 1 0 comparing the use of AIC with the use of SIC. They examine whether the use of real-time versus latest-available data matters for the choice of lag length and hence the forecasts made by each model. Their results suggest that the choice of real-time versus latest-available data matters much more for AIC than for SIC. Elliott 2002 illustrated and explained some of the Stark and Croushore results. He showed that the lag structures for real-time and revised data are likely to be different that greater persistence in the latest-available series increases those differences and that RMSEs for forecasts using revised data may be substantially less than for real-time forecasts. Monte Carlo results showed that the choices of models made using AIC or BIC is much wider using real-time data than using revised data. Finally Elliott suggested constructing forecasting models with both real-time and revised data at hand an idea we will revisit in Section 5. 4. The literature on how data revisions affect forecasts In this section we examine how data revisions affect forecasts by reviewing the most important papers in the literature. We begin by discussing how forecasts differ when using first-available compared with latest-available data. We examine whether these effects are bigger or smaller depending on whether a variable is being forecast in levels or growth rates. Then we investigate the influence data revisions have on model selection and specification. Finally we examine the evidence on the predictive content of variables when subject to revision. The key .