Macroeconomic Forecasting Using Penalized Regression Methods

Stephan Smeekes, Etiënne Wijler

Research output: Working paper / PreprintWorking paper

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We study the suitability of lasso-type penalized regression techniques when
applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying 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 morerobust to mis-specification than factor models estimated by principal components, even if the underlying DGP is a factor model. Furthermore, the penalized regression methods are demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data containing cointegrated variables, despite a deterioration of the selective capabilities. Finally, we also consider an empirical application to a large macroeconomic U.S. dataset and demonstrate that, in line with our simulations, penalized regression methods attain the best forecast accuracy most frequently.
Original languageEnglish
PublisherMaastricht University, Graduate School of Business and Economics
Publication statusPublished - 2016

Publication series

SeriesGSBE Research Memoranda

JEL classifications

  • c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
  • c53 - "Forecasting and Prediction Methods; Simulation Methods "
  • e17 - General Aggregative Models: Forecasting and Simulation: Models and Applications


  • Forecasting
  • Lasso
  • Factor Models
  • High-Dimensional Data
  • Cointegration

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