Prior Information and ARIMA Forecasting
Using the method of ARIMA forecasting with benchmarks developed in this paper, it is possible to obtain forecasts which take into account the historical information of a series, captured by an ARIMA model (Box and Jenkins, 1970), as well as partial prior information about the forecasts. Prior inform...
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Published in | Journal of forecasting Vol. 1; no. 4; pp. 375 - 383 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Chichester
John Wiley & Sons, Ltd
01.10.1982
Wiley |
Subjects | |
Online Access | Get full text |
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Summary: | Using the method of ARIMA forecasting with benchmarks developed in this paper, it is possible to obtain forecasts which take into account the historical information of a series, captured by an ARIMA model (Box and Jenkins, 1970), as well as partial prior information about the forecasts. Prior information takes the form of benchmarks. These originate from the advice of experts, from forecasts of an annual econometric model or simply from pessimistic, realistic or optimistic scenarios contemplated by the analyst of the current economic situation. The benchmarks may represent annual levels to be achieved, neighbourhoods to be reached for a given time period, movements to be displayed or more generally any linear criteria to be satisfied by the forecasted values. The forecaster may then exercise his current economic evaluation and judgement to the fullest extent in deriving forecasts, since the laboriousness experienced without a systematic method is avoided. |
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Bibliography: | ark:/67375/WNG-WK23X3XG-7 ArticleID:FOR3980010405 istex:A48F0ED104E72573DCEDAD025ACA7A955A61FA41 Pierre A. Cholette is an economist‐statistician with the Time Series Research and Analysis Staff at Statistics Canada. He has been working on seasonal adjustment, moving averages, spectral analysis of linear filters, adjustment of sub‐annual series to yearly benchmarks and on ARIMA models. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0277-6693 1099-131X |
DOI: | 10.1002/for.3980010405 |