By Clements M.P., Hendry D.F.
A significant other to fiscal Forecasting presents an obtainable and entire account of modern advancements in monetary forecasting. all the chapters has been specifically written by means of a professional within the box, bringing jointly in one quantity quite a number contrasting techniques and perspectives. Forecasting is a pragmatic enterprise, such a lot of of the chapters are aimed toward practitioners and nonspecialists.This e-book surveys a box that has accelerated swiftly lately. There aren't any different updated remedies that survey forecasting in one quantity. at the moment, the reader has little choice yet to find magazine articles and books which regularly extol the virtues of 1 process between many, or clarify only one of the various difficulties that beset financial forecasting. The significant other offers a finished account of the best techniques and modeling ideas which are in many instances hired. an intensive editorial review areas the contributions in context, and indicates their interconnections and commonalities.
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Additional resources for A Companion to Economic Forecasting
And P. Newbold (1986). Forecasting Economic Time Series, 2nd edn. New York: Academic Press. Hackl, P. H. ) (1991). Economic Structural Change, Analysis and Forecasting. Berlin: Springer-Verlag. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. F. (2001). How economists forecast. F. R. ), Understanding Economic Forecasts. : MIT Press, 15–41. F. R. ) (2001). Understanding Economic Forecasts. Cambridge, MA: MIT Press. F. and K. Juselius (2000).
1 above, future values of the exogenous variable z were assumed to be known when forecasting y. This assumption is sensible for deterministic variables such as an intercept or a linear trend. However, for most economic variables, the variables themselves would need to be forecast when forecasting y. Doing so leads directly to forecasting from systems of equations. R. 2), which is the basis for much empirical forecasting in economics. 3, the focus is on the inherent predictable uncertainty. The predictable uncertainty from estimation is calculable, both analytically and numerically, but it provides less direct insight due to the increased complexity of the models.
Chow (1960) discusses forecasts from this model in detail. This ﬁrst model (denoted M 1) is a static single equation: M 1: yt = bzt + ut ut ~ NI(0, σ 2) t = 1, . . , T, T + 1, . . 1) where yt is the dependent variable; zt is an exogenous variable; b is an unknown coefﬁcient; ut is an error term, assumed to be independently and normally distributed with a zero mean and a variance σ 2; t is the time subscript; and the estimation and forecast periods are [1, T ] and [T + 1, T + H] respectively, implying T observations for estimation and H observations being forecast.
A Companion to Economic Forecasting by Clements M.P., Hendry D.F.