IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v35y2019i3p848-867.html
   My bibliography  Save this article

Recession forecasting using Bayesian classification

Author

Listed:
  • Davig, Troy
  • Hall, Aaron Smalter

Abstract

We demonstrate the use of a Naïve Bayes model as a recession forecasting tool. The approach is closely connected with Markov-switching models and logistic regression, but also has important differences. In contrast to Markov-switching models, our Naïve Bayes model treats National Bureau of Economic Research business cycle turning points as data, rather than as hidden states to be inferred by the model. Although Naïve Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data. As a result, Naïve Bayes has a larger asymptotic error rate, but converges to the error rate more quickly than logistic regression, resulting in more accurate recession forecasts with limited data. We show that Naïve Bayes outperforms competing models and the Survey of Professional Forecasters consistently for real-time recession forecasting up to 12 months in advance. These results hold under standard error measures, and also under a novel measure that varies the penalty on false signals, depending on when they occur within a cycle; for example, a false signal in the middle of an expansion is penalized more heavily than one that occurs close to a turning point.

Suggested Citation

  • Davig, Troy & Hall, Aaron Smalter, 2019. "Recession forecasting using Bayesian classification," International Journal of Forecasting, Elsevier, vol. 35(3), pages 848-867.
  • Handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:848-867
    DOI: 10.1016/j.ijforecast.2018.08.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207018301560
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijforecast.2018.08.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeremy J. Nalewaik, 2011. "Forecasting recessions using stall speeds," Finance and Economics Discussion Series 2011-24, Board of Governors of the Federal Reserve System (U.S.).
    2. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    3. Hamilton, James D & Kim, Dong Heon, 2002. "A Reexamination of the Predictability of Economic Activity Using the Yield Spread," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 340-360, May.
    4. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    5. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
    6. Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(1), pages 1-34, February.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    9. Nalewaik, Jeremy J., 2011. "Incorporating vintage differences and forecasts into Markov switching models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 281-307.
    10. Zhihong Chen & Azhar Iqbal & Huiwen Lai, 2011. "Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach," Canadian Journal of Economics, Canadian Economics Association, vol. 44(2), pages 651-672, May.
    11. Nalewaik, Jeremy J., 2011. "Incorporating vintage differences and forecasts into Markov switching models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 281-307, April.
    12. Jefferson, Philip N, 1998. "Inference Using Qualitative and Quantitative Information with an Application to Monetary Policy," Economic Inquiry, Western Economic Association International, vol. 36(1), pages 108-119, January.
    13. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    14. Travis J. Berge, 2015. "Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 455-471, September.
    15. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    16. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
    17. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    18. Estrella, Arturo & Hardouvelis, Gikas A, 1991. "The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
    19. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    20. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
    21. Jeremy J. Nalewaik, 2012. "Estimating Probabilities of Recession in Real Time Using GDP and GDI," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 235-253, February.
    22. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    23. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
    24. Chauvet, Marcelle & Potter, Simon, 2002. "Predicting a recession: evidence from the yield curve in the presence of structural breaks," Economics Letters, Elsevier, vol. 77(2), pages 245-253, October.
    25. Troy Davig, 2008. "Detecting recessions in the Great Moderation: a real-time analysis," Economic Review, Federal Reserve Bank of Kansas City, vol. 93(Q IV), pages 5-33.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wanhai You & Yuming Huang & Chien‐Chiang Lee, 2024. "Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 473-489, March.
    2. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    3. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    4. Strauss, Ilan & Yang, Jangho, 2020. "Corporate Secular Stagnation: Empirical Evidence on the Advanced Economy Investment Slowdown," INET Oxford Working Papers 2019-16, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    5. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
    6. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    7. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.
    8. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    9. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    10. Seulki Chung, 2023. "Real-time Prediction of the Great Recession and the Covid-19 Recession," Papers 2310.08536, arXiv.org, revised May 2024.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Jiandong & Feng, Shuaizhang & Hu, Yingyao, 2021. "Misclassification errors in labor force statuses and the early identification of economic recessions," Journal of Asian Economics, Elsevier, vol. 75(C).
    2. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    3. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    4. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    5. Chauvet, Marcelle & Senyuz, Zeynep, 2016. "A dynamic factor model of the yield curve components as a predictor of the economy," International Journal of Forecasting, Elsevier, vol. 32(2), pages 324-343.
    6. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    7. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    8. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    9. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
    10. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    11. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    12. Christiansen, Charlotte & Eriksen, Jonas Nygaard & Møller, Stig Vinther, 2014. "Forecasting US recessions: The role of sentiment," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 459-468.
    13. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    14. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    15. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    16. B. De Backer & M. Deroose & Ch. Van Nieuwenhuyze, 2019. "Is a recession imminent? The signal of the yield curve," Economic Review, National Bank of Belgium, issue i, pages 69-93, June.
    17. Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
    18. Feng, Shuaizhang & Sun, Jiandong, 2020. "Misclassification-errors-adjusted Sahm Rule for Early Identification of Economic Recession," GLO Discussion Paper Series 523, Global Labor Organization (GLO).
    19. Feng, Shuaizhang & Sun, Jiandong, 2020. "Misclassification-Errors-Adjusted Sahm Rule for Early Identification of Economic Recession," IZA Discussion Papers 13168, Institute of Labor Economics (IZA).
    20. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:848-867. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.