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Forecasting the business cycle without using minimum autocorrelation factors
Author(s) -
Löfgren KarlGustaf,
Ranneby Bo,
Sjöstedt Sara
Publication year - 1993
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.3980120603
Subject(s) - autocorrelation , autoregressive model , econometrics , computer science , vector autoregression , series (stratigraphy) , business cycle , multivariate statistics , time series , mathematical optimization , statistics , mathematics , machine learning , economics , macroeconomics , paleontology , biology
We introduce a forecasting technique based on multivariate ideas previously applied in remote sensing. The approach has the trivial but nonetheless fundamental purpose of dividing the information inherent in the time series into important and unimportant. Important information is used for forecasting purposes while the unimportant is discarded. Although related to vector autoregression, giving asymptotically the same estimates, there are reasons to believe that the approach gives better precision of parameter estimates for finite samples as well as more precise predictions.