Title(题名):  Diffusion Indexes With Sparse Loadings
Authors(作者):  Johannes Tang Kristensen
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
(卷,期,年):
 July2017,Vol35,Issue3
Pages(页码):  p434-451
ISSN:  0735-0015
Abstract(摘要):  The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad hoc procedures or abandon the underlying theoretical factor model. In this article, we will take a different approach to the problem by using theleast absolute shrinkage and selection operator (LASSO) as a yariable selection method to choose between the possible variables and thus obtain sparse loadings from which f&ctors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model that is better suited for forecasting compared to the traditional principal components (PC) approach. We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on U.S. macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online.
Key words(关键词):  Factor models; Forecasting; LASSO; Principal components analysis.
Where(馆藏地):  304外文报刊阅览室
Available online
(提交时间):
 2017/12/26 10:27:01
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