Macroeconomic forecasting using penalized regression methods

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Abstract

We study the suitability of applying lasso-type penalized regression techniques to macroe-conomic forecasting with high-dimensional datasets. We consider the performances of lasso-type methods when the true dgp is a factor model, contradicting the sparsity assumptionthat underlies penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are more robust to mis-specification than factor models, even if the underlying dgp possesses a factor structure. Furthermore, the penalized regression methods can be demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data that contain cointegrated variables, despite a deterioration in their selective capabilities. Finally, we also consider an empirical applicationto a large macroeconomic u.s. Dataset and demonstrate the competitive performance of penalized regression methods.
Original languageEnglish
Pages (from-to)408-430
Number of pages23
JournalInternational Journal of Forecasting
Volume34
Issue number3
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Forecasting
  • Lasso
  • Factor models
  • High-dimensional data
  • Cointegration
  • DYNAMIC FACTOR MODELS
  • PRINCIPAL COMPONENT ANALYSIS
  • APPROXIMATE FACTOR MODELS
  • TIME-SERIES
  • ADAPTIVE LASSO
  • LARGE NUMBER
  • SELECTION
  • PREDICTORS
  • SHRINKAGE
  • AUTOREGRESSIONS

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