Đang chuẩn bị liên kết để tải về tài liệu:
Handbook of Economic Forecasting part 8

Không đóng trình duyệt đến khi xuất hiện nút TẢI XUỐNG Tải xuống

Handbook of Economic Forecasting part 8. 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 | 44 J. Geweke and C. Whiteman Note that the Harrison-Stevens approach generalized what was possible using Zellner s 1971 book but priors were still conjugate and the underlying structure was still Gaussian. The structures that could be handled were more general but the statistical assumptions and nature of prior beliefs accommodated were quite conventional. Indeed in his discussion of Harrison-Stevens Chatfield 1976 remarks that . you do not need to be Bayesian to adopt the method. If as the authors suggest the general purpose default priors work pretty well for most time series then one does not need to supply prior information. So despite the use of Bayes theorem inherent in Kalman filtering I wonder if Adaptive Forecasting would be a better description of the method. p. 231 The fact remains though that latent-variable structure of the forecasting model does put uncertainty about the parameterization on a par with the uncertainty associated with the stochastic structure of the observables themselves. 4.3. The Minnesota revolution During the mid- to late-1970 s Christopher Sims was writing what would become Macroeconomics and reality the lead article in the January 1980 issue of Economet-rica. In that paper Sims argued that identification conditions in conventional large-scale econometric models that were routinely used in non Bayesian forecasting and policy exercises were incredible - either they were normalizations with no basis in theory or based in theory that was empirically falsified or internally inconsistent. He proposed as an alternative an approach to macroeconomic time series analysis with little theoretical foundation other than statistical stationarity. Building on the Wold decomposition theorem Sims argued that exceptional circumstances aside vectors of time series could be represented by an autoregression and further that such representations could be useful for assessing features of the data even though they reproduce only the first and second .