Handbook of Economic Forecasting part 16 doc
Handbook of Economic Forecasting part 16 doc
... (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...
Handbook of Economic Forecasting part 4 doc
... terms of forecasting ability, a good Bayesian will beat a non-Bayesian,
who will do better than a bad Bayesian.
[C.W.J. Granger (1986, p. 16) ]
1. Introduction
Forecasting involves the use of information ... 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 e...
Handbook of Economic Forecasting part 5 doc
... 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...
Handbook of Economic Forecasting part 7 docx
... 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...
Handbook of Economic Forecasting part 9 docx
... 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...
Handbook of Economic Forecasting part 11 docx
... 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. (198...
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...
Handbook of Economic Forecasting part 15 docx
... 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...
Handbook of Economic Forecasting part 25 docx
... 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...
Handbook of Economic Forecasting part 26 docx
... 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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