摘要:

AbstractA procedure for estimating state space models for multivariate distributed lag processes is described. It involves singular value decomposition techniques and yields an internally balanced state space representation which has attractive properties. Following the specifications of a forecasting competition, the approach is applied to generateex‐postforecasts for US real GNP growth rates. The forecasts of the estimated state space model are compared to those of twelve econometric models and an ARIMA mode

ISSN:0277-6693    

DOI:10.1002/for.3980090302

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe dynamic linear model (DLM) with additive Gaussian errors provides a useful statistical tool that is easily implemented because of the simplicity of updating a normal model that has a natural conjugate prior. If the model is not linear or if it does not have additive Gaussian errors, then numerical methods are usually required to update the distributions of the unknown parameters. If the dimension of the parameter space is small, numerical methods are feasible. However, as the number of unknown parameters increases, the numerial methods rapidly grow in complexity and cost. This article addresses the situation where a state dependent transformation of the observations follows the DLM, buta priorithe appropriate transformation is not known. The Box‐Cox family, which is indexed by a single parameter, illustrates the methodology. A prior distribution is constructed over a grid of points for the transformation parameter. For each value of the grid the relevant parameter esitmates and forecasts are obtained for the transformed series. These quantities are then integrated by the current distribution of the transformation parameter. When a new observation becomes available, parallel Kalman filters are used to update the distributions of the unknown parameters and to compute the likelihood of the transformation parameter at each grid point. The distribution of the transformation parameter is then update

ISSN:0277-6693    

DOI:10.1002/for.3980090303

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThis paper addresses the issue of forecasting individual items within a product line; where each line includes several independent but closely related products. The purpose of the research was to reduce the overall forecasting burden by developing and assessing schemes of disaggregating forecasts of a total product line to the related individual items. Measures were developed to determine appropriate disaggregated methodologies and to compare the forecast accuracy of individual product forecasts versus disaggregated totals. Several of the procedures used were based upon extensions of the combination of forecast research and applied to disaggregations of total forecasts of product lines. The objective was to identify situations when it was advantageous to produce disaggregated forecasts, and if advantageous, which method of disaggregation to utilize. This involved identification of the general conceptual characteristics within a set of product line data that might cause a disaggregation method to produce relatively accurate forecasts. These conceptual characteristics provided guidelines for forecasters on how to select a disaggregation method and under what conditions a particular method is applicable.

ISSN:0277-6693    

DOI:10.1002/for.3980090304

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThis paper describes the application of space‐time ARMA modelling to demand‐related data from eight hotels from a single hotel chain in a large US city. Important spatial characteristics of the space‐time process are incorporated into the model using a simple weighting matrix based on driving distances between the hotels. Using a hold‐out sample, the forecasting performance of this space‐time approach was found to be superior to eight separate univariate AR

ISSN:0277-6693    

DOI:10.1002/for.3980090305

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe purpose of this paper is to investigate the applicability of a contemporary time series forecasting technique, transfer function modeling, to the problem of forecasting sectoral employment levels in small regional economies. The specific sectoral employment levels to be forecast are manufacturing, durable manufacturing, non‐durable manufacturing and non‐manufacturing employment. Due to data constraints at the small region level, construction of traditional causal econometric models is often very difficult; thus time series approaches become particularly attractive. The results suggest that transfer function models using readily available national indicator series as drivers can provide more accurate forecasts of small region sectoral employment levels than univariate time series mod

ISSN:0277-6693    

DOI:10.1002/for.3980090306

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractMany studies have shown that, in general, a combination of forecasts often outperforms the forecasts of a single model or expert. In this paper we postulate that obtaining forecasts is costly, and provide models for optimally selecting them. Based on normality assumptions, we derive a dynamic programming procedure for maximizing precision net of cost. We examine the solution for cases where the forecasters are independent, correlated and biased. We provide illustrative examples for each case.

ISSN:0277-6693    

DOI:10.1002/for.3980090307

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980090308

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980090301

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY