Macroeconomic forecasting using penalized regression methods

Stephan Smeekes and Etienne Wijler

International Journal of Forecasting, 2018, vol. 34, issue 3, 408-430

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.

Keywords: Forecasting; Lasso; Factor models; High-dimensional data; Cointegration (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (46)

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Working Paper: Macroeconomic Forecasting Using Penalized Regression Methods (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:34:y:2018:i:3:p:408-430

DOI: 10.1016/j.ijforecast.2018.01.001

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