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The Chen-Tindall system and the lasso operator: improving automatic model performance

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Abstract

Using U.S. monthly macroeconomic data, the automatic model system presented in Chen and Tindall [2016] outperforms the lasso automatic system, but the lasso is improved where Bayesian model averaging is employed to combine its forecasts with those from autoregressive schemes. The best performance is obtained using Bayesian model averaging to combine the Chen?Tindall system, the lasso, and autoregressive schemes. Performance is virtually the same using this combined approach where the elastic-net operator is substituted for the lasso. Similar overall outcomes are found for France and Germany treated as a single economic system and for Canada.

Suggested Citation

  • Jiaqi Chen & Michael Tindall, 2016. "The Chen-Tindall system and the lasso operator: improving automatic model performance," Occasional Papers 16-1, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddop:2016_001
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    References listed on IDEAS

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    1. Jiaqi Chen & Michael Tindall, 2013. "The structure of a machine-built forecasting system," Occasional Papers 13-1, Federal Reserve Bank of Dallas.
    2. Pavel Kapinos & Oscar A. Mitnik, 2016. "A Top-down Approach to Stress-testing Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(2), pages 229-264, June.
    3. repec:fip:feddop:1:x:1 is not listed on IDEAS
    4. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank.
    5. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    8. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    9. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    10. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
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