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Co‐integration constraint and forecasting: An empirical examination
Author(s) -
Lin JinLung,
Tsay Ruey S.
Publication year - 1996
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/(sici)1099-1255(199609)11:5<519::aid-jae410>3.0.co;2-q
Subject(s) - constraint (computer aided design) , unit root , trace (psycholinguistics) , econometrics , term (time) , computer science , covariance , sample (material) , forecast error , mathematics , statistics , geometry , philosophy , linguistics , physics , chemistry , chromatography , quantum mechanics
Does co‐integration help long‐term forecasts? In this paper, we use simulation, real data sets, and multi‐step‐ahead post‐sample forecasts to study this question. Based on the square root of the trace of forecasting error‐covariance matrix, we found that for simulated data imposing the ‘correct’ unit‐root constraints implied by co‐integration does improve the accuracy of forecasts. For real data sets, the answer is mixed. Imposing unit‐root constraints suggested by co‐integration tests produces better forecasts for some cases, but fares poorly for others. We give some explanations for the poor performance of co‐integration in long‐term forecasting and discuss the practical implications of the study. Finally, an adaptive forecasting procedure is found to perform well in one‐ to ten‐step‐ahead forecasts.