Macro Forecasting with Machine Learning

• Traditional time series econometric forecasting methods often provide poor macro forecasts.
• Machine learning methods provide an alternative to traditional forecasting techniques.

Methods and Background:

Coulombe, P. G., Leroux, M., Stevanovic, D., & Surprenant, S. (2020). How is machine learning useful for macroeconomic forecasting? In arXiv.

Kalamara, E., Turrell, A., Redl, C., Kapetanios, G., & Kapadia, S. (2020). Making Text Count: Economic Forecasting Using Newspaper Text. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3610770

Kotchoni, R., Leroux, M., & Stevanovic, D. (2019). Macroeconomic forecast accuracy in a data-rich environment. Journal of Applied Econometrics. https://doi.org/10.1002/jae.2725

Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á., & Zilberman, E. (2019). Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods. Journal of Business and Economic Statistics, 1–22. https://doi.org/10.1080/07350015.2019.1637745

Nyman, R., & Ormerod, P. (2017). Predicting Economic Recessions Using Machine Learning Algorithms. In arXiv.

Smalter Hall, A. (2018). Machine Learning Approaches to Macroeconomic Forecasting. The Federal Reserve Bank of Kansas City Economic Review. https://doi.org/10.18651/er/4q18smalterhall

Smeekes, S., & Wijler, E. (2018). Macroeconomic forecasting using penalized regression methods. International Journal of Forecasting, 34(3), 408–430. https://doi.org/10.1016/j.ijforecast.2018.01.001

Stock, J. H., & Watson, M. W. (2012). Generalized shrinkage methods for forecasting using many predictors. Journal of Business and Economic Statistics, 30(4), 481–493. https://doi.org/10.1080/07350015.2012.715956

Tu, Y., & Lee, T.-H. (2019). Forecasting using supervised factor models. Journal of Management Science and Engineering, 4(1), 12–27. https://doi.org/10.1016/j.jmse.2019.03.001

Uematsu, Y., & Tanaka, S. (2019). High-dimensional macroeconomic forecasting and variable selection via penalized regression. Econometrics Journal. https://doi.org/10.1111/ectj.12117

Applications:

IMF:

Bolhuis, M. A., & Rayner, B. (2020). Deus ex Machina? A Framework for Macro Forecasting with Machine Learning. IMF Working Paper. https://www.imf.org/en/Publications/WP/Issues/2020/02/28/Deus-ex-Machina-A-Framework-for-Macro-Forecasting-with-Machine-Learning-49094

Bolhuis, M. A., & Rayner, B. (2020). The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data. IMF Working Paper. https://www.imf.org/en/Publications/WP/Issues/2020/02/28/The-More-the-Merrier-A-Machine-Learning-Algorithm-for-Optimal-Pooling-of-Panel-Data-49045

Jung, J.-K., Patnam, M., & Ter-Martirosyan, A. (2018). An Algorithmic Crystal Ball: Forecasts-based on Machine Learning. IMF Working Paper. https://www.imf.org/en/Publications/WP/Issues/2018/11/01/An-Algorithmic-Crystal-Ball-Forecasts-based-on-Machine-Learning-46288

Tiffin, A. (2016). Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon. IMF Working Paper. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Seeing-in-the-Dark-A-Machine-Learning-Approach-to-Nowcasting-in-Lebanon-43779

Tiffin, A. (2019). Machine Learning and Causality: The Impact of Financial Crises on Growth. IMF Working Papers, 19(228). https://www.imf.org/en/Publications/WP/Issues/2019/11/01/Machine-Learning-and-Causality-The-Impact-of-Financial-Crises-on-Growth-48722

Central banks:

Richardson, A., Mulder, T. van F., & Vehbi, T. (2019). Nowcasting GDP using machine learning algorithms: A real-time assessment. Discussion Paper. https://www.rbnz.govt.nz/research-and-publications/discussion-papers/2019/dp2019-03