1. |
Forecasting from non‐linear models in practice |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 1-9
Jin‐Lung Lin,
C. W. J. Granger,
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摘要:
AbstractIf a simple non‐linear autoregressive time‐series model is suggested for a series, it is not straightforward to produce multi‐step forecasts from it. Several alternative theoretical approaches are discussed and then compared with a simulation study only for the two‐step case. It is suggested that fitting a new model for each forecast horizon may be a satisfactory s
ISSN:0277-6693
DOI:10.1002/for.3980130102
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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2. |
An envelope function model for forecasting athletics records |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 11-20
G. R. Dargahi‐Noubary,
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摘要:
AbstractThe purpose of this paper is to present a multiplicative model for the fastest time per year in the 400‐ and 800‐metre races. It is composed of an envelope function and a stationary time series. The proposed model is plausible since there is dependency between the fastest times of successive years and because improvements are expected to be smaller now, as compared with several years ago. Data from the years 1860 to 1988 are used for illustrat
ISSN:0277-6693
DOI:10.1002/for.3980130103
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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3. |
A non‐linear combination of experts' forecasts: A Bayesian approach |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 21-27
Luisa Tibiletti,
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摘要:
AbstractA general Bayesian approach to combiningnexpert forecasts is developed. Under some moderate assumptions on the distributions of the expert errors, it leads to a consistent, monotonic, quasi‐linear average formula. This generalizes Bordley's result
ISSN:0277-6693
DOI:10.1002/for.3980130104
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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4. |
A bayesian decision approach to model monitoring and cusums |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 29-36
P. J. Harrison,
P. P. Veerapen,
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摘要:
AbstractCumulative Sum techniques are widely used in quality control and model monitoring. A single‐sided cusum may be regarded essentially as a sequence of sequential tests which, in many cases, such as those for the Exponential Family, is equivalent to a Sequence of Sequential Probability Ratio Tests. The relationship between cusums and Bayesian decisions is difficult to establish using conventional methods. An alternative approach is proposed which not only reveals a relation but also offers a very simple formulation of the decision process involved in model monitoring. This is first illustrated for a Normal mean and then extended to other important practical cases including Dynamic Models. For V‐mask cusum graphs a particular feature is the interpretation of the distance of the V vertex from the latest plotted point in terms of the prior precision as measured in ‘equivalent’ obser
ISSN:0277-6693
DOI:10.1002/for.3980130105
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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5. |
On robust estimation of threshold autoregressions |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 37-49
Wai‐Sum Chan,
Siu‐Hung Cheung,
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摘要:
AbstractWe investigate the effects of additive outliers on the least squares (LS) estimation of threshold autoregressive models. The class of generalized‐M (GM) estimates for linear time series is modified and applied to non‐linear threshold processes. A Monte Carlo experiment is carried out to study the robust properties of these estimates. Their relative forecasting performances are also examined. The results indicate that the GM method is preferable to the LS estimation when the observations are contaminated by additive outliers. A real example is also given to illustrate the proposed met
ISSN:0277-6693
DOI:10.1002/for.3980130106
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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6. |
Bootstrapping forecast intervals: An application to AR(p) models |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page 51-66
B. D. McCullough,
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摘要:
AbstractForecast intervals typically depend upon an assumption of normal forecast errors due to lack of information concerning the distribution of the forecast. This article applies the bootstrap to the problem of estimating forecast intervals for an AR(p) model. Box‐Jenkins intervals are compared to intervals produced from a naive bootstrap and a bias‐correction bootstrap. Substantial differences between the three methods are found. Bootstrapping an AR(p) model requires use of the backward residuals which typically are not i.i.d. and hence inappropriate for bootstrap resampling. A recently developed method of obtaining i.i.d. backward residuals is employed and was found to affect the bootstrap prediction interv
ISSN:0277-6693
DOI:10.1002/for.3980130107
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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7. |
Masthead |
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Journal of Forecasting,
Volume 13,
Issue 1,
1994,
Page -
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PDF (93KB)
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ISSN:0277-6693
DOI:10.1002/for.3980130101
出版商:John Wiley&Sons, Ltd.
年代:1994
数据来源: WILEY
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