Handbook of Economic Forecasting part 12 doc

... of the choice variable and inessential additive transformations of the objective function, exhaust the class of loss-function-preserving transformations of a decision problem. 2.3. Recovery of ... the analysis of general loss functions, which preserves most of the intuition of the standard cases. Section 2.5 examines the above types of questions when we replace point fore- c...

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... enlarged the scope of models that can be brought to bear on forecasting problems using either Bayesian or non-Bayesian methods, and significantly increased the quality of economic forecasting. This ... history of how they came about and improved economic decision-making. The chapter begins in Section 2 with an exposition of Bayesian inference, empha- sizing applications of the...

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... special case of the frequentist formulation of the forecasting problem described at the end of Section 2.4.1. As such, it inherits the internal inconsis- tencies of this approach, often appearing ... context of Section 2.3, forecasting from a combination of models is straight- forward. The vector of interest ω includes the relevant future observables (y T +1 , , y T +F ), an...

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... linear combination of the elements of  Y (his exam- ple of such a function of interest is a discounted sum) will be distributed as univariate Student-t, so that expectations of such linear combinations ... applications of Metropolis within Gibbs in Bayesian forecasting models. 3.3. The full Monte We are now in a position to complete the practical Bayesian agenda for forecastin...

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... of the outcome of the conventional procedure for determining the rank of the error correction matrix. The paper offers an extensive comparison of root mean square forecasting errors for all of ... treated as a function of φ, is the kernel of a p-variate normal distri- bution. If the prior distribution of φ is Gaussian, truncated to S p , then the same is true of the produc...

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... vectors”. Journal of Economic Dynamics and Con- trol 12, 231–254. Kadiyala, K.R., Karlsson, S. (1993). Forecasting with generalized Bayesian vector autoregressions”. Journal of Forecasting 12, 365–378. Kadiyala, ... Ph.D. thesis, University of Wisconsin, Unpublished. Chapter 2 FORECASTING AND DECISION THEORY CLIVE W.J. GRANGER and MARK J. MACHINA Department of Economics, Univ...

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... Ee 1t+1 x t = 0 with out of sample prediction errors. The remainder of this section considers the implications of a test that does fail the rank condition of the theory of the previous section ... models. 6. A small number of models, nested: MSPE Analysis of nested models per se does not invalidate the results of the previous sections. A rule of thumb is: if the rank of th...

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... (2005a)]. 9. Large number of models Sometimes an investigator will wish to compare a large number of models. There is no precise definition of large. But for samples of size typical in economics research, procedures ... number of models is say in the single digits, and have a great deal of appeal when the number of models is Ch. 3: Forecast Evaluation 127 Table 3A Recommended p...

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... approach of DGT) should be of interest from the perspective of out -of- sample evaluation. For this reason, and for sake of completeness, in this section we provide out -of- sample versions of all of the ... set of moment conditions which are satisfied under the null of a particular distribution. This leads to a GMM type test. Of interest is the fact that, the tests suggested...

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... of the estimation scheme used, the contribution of parameter estimation error is asymptotically negligible. 3.3. Out -of- sample implementation of Corradi and Swanson tests We now outline out -of- sample ... following sections, application of Theorem 3.6 allows us to capture the contribution of (recur- sive) parameter estimation error to the covariance kernel of the limiting distr...

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