Handbook of Economic Forecasting part 94 doc

... sense that the probability of recession is independent of the length of the current expansion or recession, and of past values of R t . The evaluation of the probability of recession in period t ... the probability of being in a recession today is given by the sum of the rows of ζ t|t corresponding to those values of s ∗ t characterized by s t = 1, while the probability...

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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 ... AR(p) model One of the simplest models of serial correlation in ε t is an autoregression of order p. The contemporary Bayesian treatment of this problem [see Chib and Greenberg (...

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... analysis of vector ARMA models using Gibbs sampling”. Journal of Forecasting 16, 177– 194. Ravishanker, N., Ray, B.K. (1997b). “Bayesian analysis of vector ARFIMA process”. Australian Journal of Statistics ... 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. (19...

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... 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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... 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 ... (2004). “A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure”. International Jou...

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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 ... performance. 3.2. Out -of- sample implementation of Bai as well as Hong and Li tests We begin by analyzing the out -of- sample versions of Bai’s (2003) test. Define the out...

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