The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest
- Cadahia Delgado, Pedro 1
- Congregado, Emilio 1
-
Golpe, Antonio A.
1
- Vides, José Carlos 2
-
1
Universidad de Huelva
info
- 2 Universidad Complutense de Madrid. Ciencias Económicas y Empresariales. Instituto Complutense de Estudios Internacionales
ISSN: 1989-1660
Any de publicació: 2022
Volum: 7
Número: 3
Pàgines: 7-19
Tipus: Article
Altres publicacions en: International Journal of Interactive Multimedia and Artificial Intelligence
Resum
Most representative decision-tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3-month–6-month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.
Referències bibliogràfiques
- A. Estrella, “The yield curve as a leading indicator: frequently asked questions,” Federal Reserve Bank of New York, 2005.
- M. J. Holmes, J. Otero, and T. Panagiotidis, “The expectations hypothesis and decoupling of short- and long-term US interest rates: A pairwise approach,” The North American Journal of Economics and Finance, vol. 34, pp. 301-313., 2015, doi: 10.1016/j.najef.2015.09.014.
- W. Liu and E. Moench, “What predicts US recessions?,” International Journal of Forecasting, vol. 34, no. 4, 2016, doi: 10.1016/j.ijforecast.2016.02.007.
- R. J. Shiller and J. Huston McCulloch, “Chapter 13 The term structure of interest rates,” Handbook of Monetary Economics. vol. 1, pp. 627-722, 1990, doi: 10.1016/S1573-4498(05)80016-5.
- A. Estrella and G. A. Hardouvelis, “The Term Structure as a Predictor of Real Economic Activity,” The Journal of Finance, vol. 46, pp. 555-576, 1991, doi: 10.1111/j.1540-6261.1991.tb02674.x.
- E. Weber and J. Wolters, “The US term structure and central bank policy,” Applied Economics Letters, vol. 41, pp. 41-45, 2012, doi:10.1080/13504851.2011.566171.
- E. Weber and J. Wolters, “Risk and Policy Shocks on the US Term Structure,” Scottish Journal of Political Economy, vol. 19, pp. 41-45, 2013, doi: 10.1111/sjpe.12004.
- J. Y. Campbell, “Some Lessons from the Yield Curve,” Journal of economic perspectives, vol. 9, no. 3, pp. 129-152, 1995, doi: 10.1257/jep.9.3.129.
- B. S. Bernanke and A. S. Blinder, “The federal funds rate and the channels of monetary transmission,” The American Economic Review., pp. 901-921, 1992, doi: 10.2307/2117350.
- J. C. Vides, J. Iglesias, and A. A. Golpe, “The term structure under nonlinearity assumptions: New methods in time series,” in Contributions to Management Science, pp. 117-136, 2018.
- K. R. Vetzal, “A survey of stochastic continuous time models of the term structure of interest rates,” Insurance: Mathematics and Economics, vol. 14, no. 2, pp. 139-161, 1994, doi: 10.1016/0167-6687(94)00009-3.
- A. Estrella and M. R. Trubin, “The Yield Curve as a Leading Indicator: Some Practical Issues,” Current issues in Economics and Finance, vol. 12, no. 5, 2006.
- A. Estrella and F. S. Trubin, “The yield curve as a predictor of US recessions,” Current issues in economics and finance, vol. 2, no. 7, 1996.
- W. Poole, R. H. Rasche, and D. L. Thornton, “Market Anticipations of Monetary Policy Actions,” Federal Reserve Bank of St. Louis Review, vol. 84, no. 4, pp. 65-94, 2002, doi: 10.20955/r.84.65-94.
- A. Estrella and G. A. Hardouvelis, “Possible roles of the yield curve in monetary policy.” Federal Reserve Bank of New York (Ed.), Intermediate targets and indicators for monetary policy, pp. 339-362, 1990.
- M. J. Dueker, “Strengthening the Case for the Yield Curve as a Predictor of US Recessions,” Federal Reserve Bank of St. Louis Review, pp. 41-51, 1997.
- R. D. Laurent, “An interest rate-based indicator of monetary policy,” Economic Perspectives, pp. 3-14, 1988.
- C. R. Harvey, “Forecasts of Economic Growth from the Bond and Stock Markets,” Financial Analysts Journal, vol 45. no. 5, pp. 38-45, 1989, doi: 10.2469/faj.v45.n5.38.
- J. H. Stock & M. W. Watson, “New indexes of coincident and leading economic indicators,” NBER macroeconomics annual, vol. 4, pp. 351-394, 1989.
- N. F. Chen, “Financial investment opportunities and the macroeconomy,” The Journal of Finance, vol. 46, no. 2, pp. 529-554, 1991, doi: 10.1111/j.1540-6261.1991.tb02673.x.
- C. R. Harvey, “Term structure forecasts economic growth. Financial Analysts Journal”, vol. 49, no. 3, pp. 6-8, 1993, doi: 10.2469/faj.v49.n3.6.2.
- M. Dotsey, “The predictive content of the interest rate term spread for future economic growth,” FRB Richmond Economic Quarterly, vol. 84, no. 3, pp. 31-51, 1998.
- J.D. Hamilton & D.H. Kim, "A re-examination of the predictability of the yield spread for real economic activity," Journal of Money, Credit, and Banking, vol. 34, no. 2, pp. 340-360, 2002.
- A. Estrella, “Why does the yield curve predict output and inflation?,” The Economic Journal, vol. 115, no. 505, pp. 722-744, 2005, doi: 10.1111/j.1468-0297.2005.01017.x.
- A. Ang, M. Piazzesi, and M. Wei, “What does the yield curve tell us about GDP growth?,” Journal of econometrics, vol. 131, no. 1-2, pp. 359-403, 2006, doi: 10.1016/j.jeconom.2005.01.032.
- D. C. Wheelock and M. E. Wohar, “Can the term spread predict output growth and recessions? A survey of the literature,” Federal Reserve Bank of St. Louis Review, vol. 91, no. 5, pp. 419-440, 2009, doi: 10.20955/r.91.419-440.
- A. Estrella, A. P. Rodrigues, and S. Schich, “How stable is the predictive power of the yield curve? Evidence from Germany and the United States,” Review of Economics and Statistics, vol. 85, no. 3, pp. 629-644, 2003, doi:10.1162/003465303322369777.
- G. D. Rudebusch and J. C. Williams, “Forecasting recessions: The puzzle of the enduring power of the yield curve,” J ournal of Business & Economic Statistics, pp. 492-503, 2009, doi: 10.1198/jbes.2009.07213.
- H. Nyberg, “Dynamic probit models and financial variables in recession forecasting,” Journal of Forecasting, 2010, doi: 10.1002/for.1161.
- Lahiri, K., Monokroussos, G., & Zhao, Y. “The yield spread puzzle and the information content of SPF forecasts,” Economics Letters, vol. 118, no. 1, pp. 219-221, 2013.
- M. Chinn and K. Kucko, “The Predictive Power of the Yield Curve Across Countries and Time,” International Finance, vol. 18, no. 2, pp. 129-156, 2015, doi: 10.1111/infi.12064.
- A. Evgenidis, A. Tsagkanos, and C. Siriopoulos, “Towards an asymmetric long run equilibrium between stock market uncertainty and the yield spread. A threshold vector error correction approach,” Research in International Business and Finance, 2017, doi: 10.1016/j.ribaf.2016.08.002.
- B. Gebka and M. E. Wohar, “The predictive power of the yield spread for future economic expansions: Evidence from a new approach,” Economic Modelling, 2018, doi: 10.1016/j.econmod.2018.06.018.
- A., Evgenidis, S. Papadamou, and C. Siriopoulos, “The yield spread’s ability to forecast economic activity: What have we learned after 30 years of studies?,” Journal of Business Research, vol. 106, pp. 221-232.
- S. Ng, “Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data,” in Advances in Economics and Econometrics, 2017.
- T. J. Berge, “Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle,” Journal of Forecasting, 2015, doi: 10.1002/for.2345.
- P. Gogas, T. Papadimitriou, M. Matthaiou, and E. Chrysanthidou, “Yield Curve and Recession Forecasting in a Machine Learning Framework,” Computational Economics, 2015, doi: 10.1007/s10614-014-9432-0.
- P. Gogas, T. Papadimitriou and E. Chrysanthidou, “Yield curve point triplets in recession forecasting,” International Finance, 2015, doi:10.1007/s10614-014-9432-0.
- J. Döpke, U. Fritsche, and C. Pierdzioch, “Predicting recessions with boosted regression trees,” International Journal of Forecasting, 2017, doi: 10.1016/j.ijforecast.2017.02.003.
- S. D. Vrontos, J. Galakis and I. D. Vrontos, “Modeling and predicting US recessions using machine learning techniques,” International Journal of Forecasting, 2020, doi: 1016/j.ijforecast.2020.08.005
- K. Bluwstein, M. Buckmann, A. Joseph, M. Kang, S. Kapadia, and Ö. Simsek, “Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3520659.
- P. Wei, Z. Lu, and J. Song, “Variable importance analysis: A comprehensive review,” Reliability Engineering and System Safety. 2015, doi: 10.1016/j. doi: 10.1016/j.ress.2015.05.018.
- Y. H. Yun, B. C. Deng, D. S. Cao, W. T. Wang, and Y. Z. Liang, “Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery,” Analytica chimica acta, 2016, doi:10.1016/j.aca.2015.12.043.
- S. Yang, W. Tian, Y. Heo, Q. Meng, and L. Wei, “Variable Importance Analysis for Urban Building Energy Assessment in the Presence of Correlated Factors,” Procedia Engineering, 2015, doi: 10.1016/j.proeng.2015.08.1069.
- E. Vladislavleva, T. Friedrich, F. Neumann, and M. Wagner, “Predicting the energy output of wind farms based on weather data: Important variables and their correlation,” Renewable Energy, 2013, doi: 10.1016/j.renene.2012.06.036.
- I. R. Dohoo, C. Ducrot, C. Fourichon, A. Donald, and D. Hurnik, “An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies,” Preventive veterinary medicine, 1997, doi: 10.1016/S0167-5877(96)01074-4.
- S. Tufféry, “Data Mining and Statistics for Decision Making,” John Wiley & Sons, 2011.
- A. J. Ferreira and M. A. T. Figueiredo, “Boosting algorithms: A review of methods, theory, and applications,” in Ensemble Machine Learning: Methods and Applications, 2012.
- Q. Sun and B. Pfahringer, “Bagging ensemble selection,” In Australasian Joint Conference on Artificial Intelligence, 2011, doi: 10.1007/978-3-642-25832-9_26.
- T. K. Ho, “Random decision forests,” In Proceedings of 3rd international conference on document analysis and recognition, vol. 1, pp. 278-282, 1995. doi: 10.1109/ICDAR.1995.598994.
- E. M. Kleinberg, “Stochastic discrimination,” Annals of Mathematics and Artificial intelligence, vol. 1, no. 1-4, pp. 278-282, 1990, doi: 10.1007/BF01531079.
- E. M. Kleinberg, “On the algorithmic implementation of stochastic discrimination,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, doi: 10.1109/34.857004.
- L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
- M. N. Wright and A. Ziegler, “Ranger: A fast implementation of random forests for high dimensional data in C++ and R,” Journal of Statistical Software, 2017, doi: 10.18637/jss.v077.i01.
- J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, 2001, doi: 10.1214/aos/1013203451.
- Tianqi Chen and Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System,” 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, https://arxiv.org/abs/1603.02754
- C. Otte, “Safe and interpretable machine learning: A methodological review,” Studies in Computational Intelligence, 2013, doi: 10.1007/978-3-642-32378-2_8.
- P. Wei, Z. Lu, and J. Song, “Variable importance analysis: A comprehensive review,” Reliability Engineering and System Safety. 2015, doi: 10.1016/j.ress.2015.05.018.
- C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: Illustrations, sources and a solution,” BMC Bioinformatics, 2007, doi: 10.1186/1471-2105-8-25.
- C. Strobl, A. L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importance for random forests,” BMC Bioinformatics, 2008, doi:10.1186/1471-2105-9-307.
- S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nature machine intelligence, 2020, doi:10.1038/s42256-019-0138-9.
- S. Lipovetsky and M. Conklin, “Analysis of regression in game theory approach,” Applied Stochastic Models in Business and Industry, 2001, doi: 10.1002/asmb.446.
- S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” In Advances in neural information processing systems, pp. 4765-4774, 2017.
- H. Deng, “Interpreting tree ensembles with intrees,” International Journal of Data Science and Analytics, vol. 7, no. 4, pp. 277-287, 2019. doi: 10.1007/s41060-018-0144-8.
- A. Estrella, and F. S. Mishkin, “Predicting US recessions: Financial variables as leading indicators,” Review of Economics and Statistics, vol. 80, no. 1, pp. 45-61, 1998.
- D. R. Cameron, “The expansion of the public economy: A comparative analysis,” The American Political Science Review, pp. 1243-1261, 1978.