Handbook of Economic Forecasting part 89 docx

... k), should be the realization of a chi-square distribution with K degrees of freedom. Of course, this K degree of freedom test ignores the fact that the parameters in the density forecasting model typically ... Chapter 3 of this Handbook. Interval, quan- tile and Value-at-Risk forecast evaluation is developed further in Christoffersen (1998, 2003), Christoffersen, Hahn and Inoue...

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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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... Honor of Arnold Zellner. Wiley, New York, pp. 243–256. McNees, S.K. (1975). “An evaluation of economic forecasts”. New England Economic Review, 3–39. McNees, S.K. (1986). Forecasting accuracy of ... Ph.D. thesis, University of Wisconsin, Unpublished. Chapter 2 FORECASTING AND DECISION THEORY CLIVE W.J. GRANGER and MARK J. MACHINA Department of Economics, University of Calif...

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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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... 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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... →∞. P ROOF. For the proof of part (ii), see Appendix B.  Note that under the null, the least favorable case arises when E(g(u 1,t+1 ) − g(u k,t+1 )) = 0, ∀k. In this case, the distribution of S P coincides ... check whether a more accurate forecasting model can be constructed by including possibly unknown (non)linear functions of the past of the process or of the past of some...

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... Journal of Forecasting 6, 187–198. Harvey, A.C., Todd, P.H.J. (1983). Forecasting economic time series with structural and Box–Jenkins models (with discussion)”. Journal of Business and Economic ... analysis of a cross-section of time series by stochas- tically convergent parameter regression”. Annals of Economic and Social Measurement 2, 399–428. Schweppe, F. (1965). “Eval...

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... w Sn+s is a vector of I(0) variables that can include lags of  1 y Sn+s . Three forecasting models can be considered: the complete true model given by (41), the long- run forecasting model of y Sn+s = ... forecast horizon of h = 8, or two years. In this sense, the specification of seasonality as being of the nonstationary stochastic or deterministic form may not be of great...

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