TAILIEUCHUNG - Handbook of Economic Forecasting part 74

Handbook of Economic Forecasting part 74. 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 | 704 E. Ghysels et al. Implicitly one therefore has a prediction model for the non-seasonal components y f and irregular y appearing in Equation 79 . For example how many unit roots is y f assumed to have when seasonal adjustment procedures are applied and is the same assumption used when subsequently seasonally adjusted series are predicted One might also think that the same time series model either implicitly or explicitly used for y f yt should be subsequently used to predict the seasonally adjusted series. Unfortunately that is not the case since the seasonally adjusted series equals y f y t et where the latter is an extraction error . the error between the true non-seasonal and its estimate. However this raises another question scantly discussed in the literature. A time series model for yf y t embedded in the seasonal adjustment procedure namely used to predict future raw data and a time series model for et properties often known and determined by the extraction filter implies a model for y f y et. To the best of our knowledge applied time series studies never follow a strategy that borrows the non-seasonal component model used by statistical agencies and adds the stochastic properties of the extraction error to determine the prediction model for the seasonally adjusted series. Consequently the model specification by statistical agencies in the course of seasonal adjusting a series is never taken into account when the adjusted series are actually used in forecasting exercises. Hence seasonal adjustment and forecasting seasonally adjusted series are completely independent. In principle this ought not to be the case. To conclude this subsection it should be noted however that in some circumstances the filtering procedure is irrelevant and therefore the issues discussed in the previous paragraph are also irrelevant. The context is that of linear regression models with linear seasonal adjustment filters. This setting was originally studied by Sims 1974 and .

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