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1. 1. Factor-Augmented Forecasting for High-Dimensional Data

   Författare :Ying Pang; Martin Sköld; Martin Singull; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; High-Dimensional Data; Factor-Augmented Forecasting; Principal Component; Lasso; Cross Validation; Multi-Level Factor; Predictive Performance; matematisk statistik; Mathematical Statistics;

   Sammanfattning : In this thesis, we take a critical look at the factor-augmented forecast models, when a large number of time series variables available can provide the vital information for prediction. We discuss how to describe the commonality and idiosyncrasy of high-dimensional data by a handful of factors in various levels, and how to improve the predictive performance using these factors as augmented predictors. LÄS MER