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Forecasting temporally aggregated vector ARMA processes
Author(s) -
Lütkepohl Helmut
Publication year - 1986
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980050202
Subject(s) - autoregressive–moving average model , series (stratigraphy) , autoregressive model , econometrics , time series , process (computing) , autoregressive integrated moving average , measure (data warehouse) , computer science , moving average , statistics , mathematics , data mining , operating system , paleontology , biology
If interest centres on forecasting a temporally aggregated multiple time series and the generation process of the disaggregate series is a known vector ARMA (autoregressive moving average) process then forecasting the disaggregate series and temporally aggregating the forecasts is at least as efficient, under a mean squared error measure, as forecasting the aggregated series directly. Necessary and sufficient conditions for equality of the two forecasts are given. In practice the data generation process is usually unknown and has to be determined from the available data. Using asymptotic theory it is shown that also in this case aggregated forecasts from the disaggregate process will usually be superior to forecasts obtained from the aggregated process.