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Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models

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  • Li, Jiahan
  • Chen, Weiye

Abstract

In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark.

Suggested Citation

  • Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
  • Handle: RePEc:eee:intfor:v:30:y:2014:i:4:p:996-1015
    DOI: 10.1016/j.ijforecast.2014.03.016
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    Cited by:

    1. Jorge A Chan-Lau, 2017. "Lasso Regressions and Forecasting Models in Applied Stress Testing," IMF Working Papers 17/108, International Monetary Fund.
    2. repec:eee:intfor:v:33:y:2017:i:3:p:627-651 is not listed on IDEAS
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    4. repec:eee:ejores:v:264:y:2018:i:2:p:558-569 is not listed on IDEAS
    5. Paolo Andreini & Donato Ceci, 2019. "A Horse Race in High Dimensional Space," CEIS Research Paper 452, Tor Vergata University, CEIS, revised 14 Feb 2019.
    6. repec:eee:intfor:v:35:y:2019:i:2:p:555-572 is not listed on IDEAS
    7. repec:eee:intfor:v:34:y:2018:i:3:p:408-430 is not listed on IDEAS
    8. repec:eee:eneeco:v:65:y:2017:i:c:p:411-423 is not listed on IDEAS
    9. repec:eee:ecolet:v:169:y:2018:i:c:p:1-6 is not listed on IDEAS
    10. Alessi, Lucia & Balduzzi, Pierluigi & Savona, Roberto, 2019. "Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data," Working Papers 2019-03, Joint Research Centre, European Commission (Ispra site).
    11. repec:ptu:bdpart:e201806 is not listed on IDEAS
    12. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks; Potential Pitfalls and a Simple Solution," IMF Working Papers 17/107, International Monetary Fund.
    13. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    14. repec:bkr:journl:v:78:y:2019:i:2:p:67-93 is not listed on IDEAS

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