Diffusion Indexes With Sparse Loadings

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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 the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors 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.

Original languageEnglish
JournalJournal of Business and Economic Statistics
Volume35
Issue3
Pages (from-to)434-451
Number of pages18
ISSN0735-0015
DOIs
Publication statusPublished - 2017

    Research areas

  • Factor models, Forecasting, LASSO, Principal components analysis, PRINCIPAL COMPONENT ANALYSIS, APPROXIMATE FACTOR MODELS, SELECTION, REGULARIZATION, ESTIMATORS, REGRESSION, FORECASTS, NUMBER

See relations at Aarhus University Citationformats

ID: 121437176