Handbook of Economic Forecasting part 4 doc

... historical perspective 41 4. 1. In the beginning, there was diffuseness, conjugacy, and analytic work 41 4. 2. The dynamic linear model 43 4. 3. The Minnesota revolution 44 4. 4. After Minnesota: Subsequent ... 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...

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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 ... Y o T ,A  . The extension of both the quantile and linear-exponential loss functions to the case of a vector function of interest ω is straightforward. Ch. 1: Bayesian Forecas...

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... the violation of the principle of relevant conditioning, as discussed in the conclusions of Sections 2 .4. 2 and 2 .4. 3. The difficulty with exogenous variables is grounded in vio- lation of the principle of ... (48 ) as (50)p  θ ∗ (1) , θ ∗ (2) | I  r  θ ∗ (2) | θ ∗ (1) ,H 2  . Finally, recalling (43 ),thesumof (49 ) and (50) is p(θ ∗ (1) , θ ∗ (2) | I), thus establish- ing (44...

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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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... Whiteman, C.H. (19 94) . “Supplanting the Minnesota prior – forecasting macroeconomic time series using real business-cycle model priors”. Journal of Monetary Economics 34, 49 7–510. Iowa Economic Forecast, ... 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...

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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 ... bad good 4 good bad bad 5 bad bad good 6 bad bad bad ⎫ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎭ H A 5−10 = 1 4 F A 5−10 = 0 KS A 5−10 = 1 4 ⎫ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎭ H B 5−10 = 3 4 F B 5−10 = 1 KS B 5−10 =...

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... P −1 T  t=R  ˆy 1t+1 −ˆy 2t+1  2  (6.10)≡ˆσ 2 1 −  ˆσ 2 2 -adj  . 7 Note that (4. 5) and the left-hand side of (6.8) are identical, but that Section 4 recommends the use of (4. 5) while the present section recommends against use of (6.8). At the risk of beating ... one can swear off MSPE. This is discussed in the next section. 7. A small number of models, nested, Part II L...

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... consumption: An analysis of causality”. Econometrica 48 , 1 149 –1168. Avramov, D. (2002). “Stock return predictability and model uncertainty”. Journal of Financial Economics 64, 42 3 45 8. Chao, J., Corradi, ... Galvao, A.B. (20 04) . “A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure”. Internatio...

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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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... application of Theorem 3.6 allows us to capture the contribution of (recur- sive) parameter estimation error to the covariance kernel of the limiting distribution of various statistics. 3 .4. 2. V 1P,J and ... of the estimation scheme used, the contribution of parameter estimation error is asymptotically negligible. 3.3. Out -of- sample implementation of Corradi and Swanson test...

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