By Clements M.P., Hendry D.F.
A significant other to fiscal Forecasting offers an available and complete account of contemporary advancements in fiscal forecasting. all the chapters has been especially written via a professional within the box, bringing jointly in one quantity quite a number contrasting ways and perspectives. Forecasting is a realistic enterprise, such a lot of of the chapters are aimed toward practitioners and nonspecialists.This ebook surveys a box that has elevated quickly lately. There aren't any different updated remedies that survey forecasting in one quantity. at the present, the reader has little alternative yet to search out magazine articles and books which frequently extol the virtues of 1 strategy between many, or clarify only one of the numerous difficulties that beset monetary forecasting. The significant other presents a accomplished account of the top methods and modeling techniques which are commonly hired. an in depth editorial evaluate locations the contributions in context, and exhibits their interconnections and commonalities.
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Additional resources for A Companion to Economic Forecasting
At a more prosaic level, forecast uncertainty depends upon the variable being forecast, the type of model used for forecasting, the information available for constructing forecasts, and the economic process actually determining the variable being forecast. On the ﬁrst, some variables may be inherently more difﬁcult to forecast than others. For instance, imports and exports each might be highly predictable, and good models might exist for forecasting them. The trade balance – that is, the value of exports minus imports – might be quite difﬁcult to forecast.
T, T + 1, . . 16) where b1 is the intercept and b2 is the coefﬁcient on the trend t. The forecasts are: 0T+h = b1 + b2 · (T + h) h = 1, . . 17) so the forecast errors are eT+h = uT+h h = 1, . . 18) var(eT+h) = σ 2 h = 1, . . , H. 19) and the MSFE is As in the simpler static model with only an intercept, the (asymptotic) MSFE does not depend upon the forecast horizon h. 16) is the DGP. 17), as is common in many econometrics software packages. In general, the predicted MSFE differs from the actual MSFE, as when the assumed model is not the DGP; see Clements and Hendry (2001).
5 considers various uses of predictable forecast uncertainty in economic modeling. 6 concludes. Some preliminary comments will aid following the presentation below. This chapter presupposes an understanding of “how economists forecast” on the level of the discussions in Granger (1989) and Hendry and Ericsson (2001). For the most part, the current chapter restricts itself to time series and econometric models as the tools for forecasting itself, where these models are assumed to be well-speciﬁed.