摘要:

AbstractThis paper comprises an editorial review for the Special Issue on Combining Forecasts. It gives a background to the current growth of interest in this topic and speculates upon some of the reasons for this popularity. Some of the main methodological issues in practice are also described.

ISSN:0277-6693    

DOI:10.1002/for.3980080302

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractThe combination of forecasts is a simple, pragmatic and sensible way to possibly produce better forecasts. Simple extensions of the original idea involve the use of various available ‘forecasts’ even if some are rather inefficient. Some unsolved questions relate to combining forecasts with horizons longer than one period. More complicated extensions are associated with ‘encompassing’ and the combination of confidence intervals or quantiles. The relevance of information sets is emphasized in both the underlying theory and the interpretation of combi

ISSN:0277-6693    

DOI:10.1002/for.3980080303

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractThis paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.

ISSN:0277-6693    

DOI:10.1002/for.3980080304

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractThis paper will motivate alternative combining schemes, provide a statistic for comparing alternative combinations out‐of‐sample and provide an example demonstrating these techniques. The evidence suggests the proposed procedures are likely to do no worse than other approaches and promise to do better under circumstances commonly encountered with economic data: integrated series and contaminated d

ISSN:0277-6693    

DOI:10.1002/for.3980080305

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractWe provide a general Bayesian model for combining forecasts from experts (or forecasting models) who might be biased and correlated with each other. The combination procedure involves debiasing and then combining unbiased forecasts. We also provide a sequential method for learning about the forecasters' biases in the process of combining information from them.

ISSN:0277-6693    

DOI:10.1002/for.3980080306

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractAn optimal univariate forecast, based on historical and additional information about the future, is obtained in this paper. Its statistical properties, as well as some inferential procedures derived from it, are indicated. Two main situations are considered explicitly: (1) when the additional information imposes a constraint to be fulfilled exactly by the forecasts and (2) when the information is only a conjecture about the future values of the series or a forecast from an alternative model. Theoretical and empirical illustrations are provided, and a unification of the existing methods is also attempted.

ISSN:0277-6693    

DOI:10.1002/for.3980080307

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractIn combining economic forecasts a problem often faced is that the individual forecasts display some degree of dependence. We discuss latent root regression for combining collinear GNP forecasts. Our results indicate that latent root regression produces more efficient combining weight estimates (regression parameter estimates) than ordinary least squares estimation (OLS), although out‐of‐sample forecasting performance is comparable to

ISSN:0277-6693    

DOI:10.1002/for.3980080308

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractThis paper evaluates six optimal and fourad hocrecursive combination methods on five actual data sets. The performance of all methods is compared to the mean and recursive least squares. A modification to one method is proposed and evaluated. The recursive methods were found to be very effective from start‐up on two of the data sets. Where the optimal methods worked well so did thead hocones, suggesting that often combination methods allowing ‘local bias’ adjustment may be preferable to the mean forecast and comparable to the optimal me

ISSN:0277-6693    

DOI:10.1002/for.3980080309

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractWhile there is general agreement that a linear combination of forecasts can outperform the individual forecasts, there is controversy about the appropriateness of the combination method to be used in a given situation. Hence, in any given application it may be more beneficial to combine different sets of combined forecasts rather than picking one of them. This paper introduces the concept ofN‐step combinations of forecasts which involves combining the combined forecasts obtained from different combination procedures used at the preceding step. Using quarterly GNP data, evidence supporting the increase in the accuracy of the one‐period‐aheadex‐anteforecasts as the combination step increases is provided. The MSE, MAE, MAPE and their corresponding standard deviations are used to evaluate the accuracy of the forecasts o

ISSN:0277-6693    

DOI:10.1002/for.3980080310

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractDeterming the best combination of competing forecasts is a problem that has been approached primarily from a statistical viewpoint. An alternative method is presented which is based on two behavioral considerations. First, the major consumers of forecasts, decision makers and managers exhibit a form of risk/regret whereby they assign different costs to different types of forecast error and prefer forecasts that are biased in favour of avoiding ‘high‐cost’ errors. Second, the models from which forecasts are obtained are more often employed in a multi‐step prediction mode even though their parameters have been chosen to minimize only one‐step prediction error variance. The first consideration leads us to a finite state representation of error‐type propagation. Then this is used in concert with the second consideration to formulate a quadratic program whose solution yields the error‐type distribution most preferred by the decision maker. Finally, this vital information permits us to determine the optimal forecast combination. We illustrate the resulting methodology using several time series from the compilation of 1001 series used in Makridakiset al.(1982) and from the NBER forecast databank compiled by Zar

ISSN:0277-6693    

DOI:10.1002/for.3980080311

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY