摘要:

AbstractNumerous theoretical models suggests that business cycles involve nonlinear processes. In this paper we examine whether two parametric, nonlinear time‐series models—the bilinear and threshold models—can exploit apparent non‐linearity in industrial production to provide forecasts superior to those derived from the standard autoregressive

ISSN:0277-6693    

DOI:10.1002/for.3980140402

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractThere is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.

ISSN:0277-6693    

DOI:10.1002/for.3980140403

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractIn the case of US national accounts the data are revised for the first few years and every decade, which implies that we do not really have the final data. In this paper we aim to predict the final data, using the preliminary data and/or the revised data. The following predictors are introduced and derived from a context of the non‐linear filtering or smoothing problem, which are: (1) prediction of the final data of timetgiven the preliminary data up to timet‐ 1, (2) prediction of the final data of timetgiven the preliminary data up to timet, (3) prediction of the final data of timetgiven the preliminary data up to timeT, (4) prediction of the final data of timetgiven the revised data up to timet‐1 and the preliminary data up to timet‐ 1, and (5) prediction of the final data of timetgiven the revised data up to timet‐1 and the preliminary data up to timet. It is shown that (5) is the best predictor but not too different from (3). The prediction problem is illustrated using US per capita consump

ISSN:0277-6693    

DOI:10.1002/for.3980140404

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractOne of the major constraints on the use of back propagation neural networks as a practical forecasting tool is the number of training patterns needed. We propose a methodology that reduces the data requirements. The general idea is to use the Box‐Jenkins model in an exploratory phase to identify the 'lag components' of the series, to determine a compact network structure with one input unit for each lag, and then apply the validation procedure. This process minimizes the size of the network and consequently the data required to train the network. The results obtained in eight studies show the potential of the new methodology as an alternative to the traditional time‐series mod

ISSN:0277-6693    

DOI:10.1002/for.3980140405

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractThis paper presents a composite method for non‐stationary economic forecasts, incorporating reconfigurable exponential trend extraction and linear combinations of parabolic and spectral estimators for short‐term prediction. The method automatically identifies the points of time series misalignment induced by sharp environmental changes. An application to the problem of hard currency exchange rate prediction in Russia is presen

ISSN:0277-6693    

DOI:10.1002/for.3980140406

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractUsing the 'standard' approach to forecasting in the vector autoregressive moving average model, we establish basic general results on exact finite sample forecasts and their mean squared error matrices. Comparison between the exact and conditional methods of initiating the finite sample forecast calculations is presented, and a few illustrative cases are given.

ISSN:0277-6693    

DOI:10.1002/for.3980140407

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980140401

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY