By Graham Elliott, Allan Timmermann
Financial forecasting includes identifying basic but strong types to top approximate hugely advanced and evolving data-generating tactics. This poses precise demanding situations for researchers in a bunch of sensible forecasting occasions, from forecasting price range deficits and assessing monetary hazard to predicting inflation and inventory industry returns. monetary Forecasting offers a entire, unified method of assessing the prices and merits of alternative tools presently on hand to forecasters.
This textual content ways forecasting difficulties from the viewpoint of choice thought and estimation, and demonstrates the profound implications of this strategy for a way we comprehend variable choice, estimation, and mix equipment for forecasting types, and the way we review the ensuing forecasts. either Bayesian and non-Bayesian tools are lined extensive, as are various state of the art concepts for generating element, period, and density forecasts. The e-book gains distinctive shows and empirical examples of a variety of forecasting tools and exhibits how you can generate forecasts within the presence of large-dimensional units of predictor variables. The authors pay unique recognition to how estimation errors, version uncertainty, and version instability impact forecasting performance.
• provides a entire and built-in method of assessing the strengths and weaknesses of other forecasting methods
• techniques forecasting from a call theoretic and estimation perspective
• Covers Bayesian modeling, together with equipment for producing density forecasts
• Discusses version choice tools in addition to forecast combinations
• Covers a wide variety of nonlinear prediction types, together with regime switching types, threshold autoregressions, and types with time-varying volatility
• good points quite a few empirical examples
• Examines the most recent advances in forecast evaluation
• crucial for practitioners and scholars alike
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Additional resources for Economic Forecasting
The loss function is symmetric, bowl shaped, and differentiable everywhere except at 0. It is again unbounded. However, the penalty to large forecast errors increases linearly rather than quadratically as for MSE loss. 13) for 0 < α < 1. Positive forecast errors are assigned a (relative) weight of α, while negative errors get a weight of 1 − α. The greater is α, the bigger the loss from positive forecast errors, and the smaller the loss from negative errors. Again, this loss function forms a homogeneous class for all positive values of a.
This choice reﬂects the difﬁculties in putting numbers on the relative cost of over- and underpredictions. Construction of a loss function requires a deeper understanding of the forecaster’s objectives and this may be difﬁcult to accomplish. Still, the implicit choice of MSE loss by the majority of studies in the forecasting literature seems difﬁcult to justify on economic grounds. As noted by Granger and Newbold (1986, page 125), “an assumption of symmetry about the conditional mean. . is likely to be an easy one to accept.
12). 1 plots lin-lin loss against squared error loss. 5, and so corresponds to MAE loss. COM 利得图书馆 22 • Chapter 2 large errors are costlier under MSE loss. 25, so negative forecast errors are three times as costly as positive errors, reﬂected in the steeper slope of the loss curve for e < 0. 75 and so positive forecast errors are three times costlier than negative errors. 4 Linex Loss Linear-exponential, or Linex, loss takes the form L (e) = a1 (exp(a2 e) − a2 e − 1), a2 = 0, a1 > 0. 14) Linex loss is differentiable everywhere, but is not symmetric.