دورية أكاديمية
Sparse structures with LASSO through principal components: International Journal of Forecasting
| العنوان: | Sparse structures with LASSO through principal components: International Journal of Forecasting |
|---|---|
| المؤلفون: | Jokubaitis, Saulius, Celov, Dmitrij, Leipus, Remigijus |
| المصدر: | International journal of forecasting, Amsterdam : Elsevier BV, 2021, vol. 37, iss. 2, p. 759-776 ; ISSN 0169-2070 ; eISSN 1872-8200 |
| سنة النشر: | 2021 |
| المجموعة: | LAEI VL (Lithuanian Institute of Agrarian Economics Virtual Library) / LAEI VB (Lietuvos agrarinės ekonomikos institutasvirtualią biblioteką) |
| مصطلحات موضوعية: | Nowcasting, LASSO, Adaptive LASSO, Relaxed LASSO, Principal components analysis, Variable selection, GDP components |
| الوصف: | This paper examines the use of sparse methods to forecast the real (in the chain-linked volume sense) expenditure components of the US and EU GDP in the short-run sooner than national statistics institutions officially release the data. We estimate currentquarter nowcasts, along with one- and two-quarter forecasts, by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of monthly datasets by assuming sparse structures of leading indicators capable of adequately explaining the dynamics of the analyzed data. For variable selection and estimation of the forecasts, we use LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis to improve the forecasting performance. We evaluated the forecasting performance by conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports, and exports over a sample from 2005– 2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and identify reasonable subsets of explanatory variables. The proposed combination of LASSO and principal components further improves the forecast accuracy. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | http://vu.lvb.lt/VU:ELABAPDB85596569&prefLang=en_USTest |
| الإتاحة: | https://doi.org/10.1016/j.ijforecast.2020.09.005Test http://vu.lvb.lt/VU:ELABAPDB85596569&prefLang=en_USTest |
| رقم الانضمام: | edsbas.8775DAA0 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |