摘要:

AbstractMultistep prediction error methods for linear time series models are considered from both a theoretical and a practical standpoint. The emphasis is on autoregressive moving‐average (ARMA) models for which a multistep prediction error estimation method (PEM) is developed. The results of a Monte Carlo simulation study aimed at establishing the possible merits of the multistep PEM are presente

ISSN:0277-6693    

DOI:10.1002/for.3980080402

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractA constrained least squares method is developed for the estimation of the effects of an unknown intervening causal factor in regression analysis, when the unknown factor shifts the regression hyperplane monotonically upwards (downwards) over time. As an illustration, we estimate the price elasticity of cigarettes in the USA and the systematic shifts of the demand curve for cigarettes during the time period 1964‐86 (these shifts presumably reflecting the heightened awareness of the general public of the potential dangers of smoking

ISSN:0277-6693    

DOI:10.1002/for.3980080403

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractThis paper uses multivariate time series models to specify the maritime steel traffic flow in the port of Antwerp. The time series considered are the total outgoing and total incoming maritime steel traffic and the total steel production in the EEC. The obtained time series models provide useful insight into the general behaviour of the maritime steel traffic flow during the period 1971–82. In particular, they provide a quantitative interpretation of important changes which took place in the European steel industry during that period. The multivariate time series models produce forecasts which are a substantial improvement over those obtained by univariate time series models. This is especially the case for the series of total incoming maritime steel traffic in the port of Antwerp, when differencing and transformation of the original data are applie

ISSN:0277-6693    

DOI:10.1002/for.3980080404

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

摘要:

AbstractVariance intervention is a simple state‐space approach to handling sharp discontinuities of level or slope in the states or parameters of models for non‐stationary time‐series. It derives from earlier procedures used in the 1960s for the design of self‐adaptive, state variable feedback control systems. In the alternative state‐space forecasting context considered in the present paper, it is particularly useful when applied to structural time series models. The paper compares the variance intervention procedure with the related ‘subjective intervention’ approach proposed by West and Harrison in a recent issue of theJournal of Forecasting, and demonstrates it efficacy by application to various time‐series data, including those used by We

ISSN:0277-6693    

DOI:10.1002/for.3980080405

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980080406

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980080407

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980080401

出版商:John Wiley&Sons, Ltd.    

年代:1989

数据来源: WILEY