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Dating U.S. Business Cycles with Macro Factors

Author

Listed:
  • Sebastian Fossati

    (University of Alberta, Department of Economics)

Abstract

A probit model is used to show that latent common factors estimated by principal components from a large number of macroeconomic time series have important predictive power for NBER recession dates. A pseudo out-of-sample forecasting exercise shows that predicted recession probabilities consistently rise during subsequently declared NBER recession dates. The latent variable in the factor-augmented probit model is interpreted as an index of real business conditions which can be used to assess the strength of an expansion or the depth of a recession.

Suggested Citation

  • Sebastian Fossati, 2011. "Dating U.S. Business Cycles with Macro Factors," Working Papers 2011-05, University of Alberta, Department of Economics.
    • Handle: RePEc:ris:albaec:2011_005
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    File URL: https://sites.ualberta.ca/~econwps/2011/wp2011-05.pdf
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    Citations

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    Cited by:

    1. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
    2. Manzoor Ahmad & Zahoor Ul Haq & Javed Iqbal & Shehzad Khan, 2023. "Dating the Business Cycles: Research and Development (R&D) Expenditures and New Knowledge Creation in OECD Economies over the Business Cycles," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(4), pages 3929-3973, December.
    3. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    4. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    5. repec:wsr:ecbook:2011:i:iii-007 is not listed on IDEAS
    6. Sebastian Fossati, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-09, University of Alberta, Department of Economics.
    7. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    8. Alexander James & Yaser S. Abu-Mostafa & Xiao Qiao, 2019. "Nowcasting Recessions using the SVM Machine Learning Algorithm," Papers 1903.03202, arXiv.org, revised Jun 2019.
    9. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
    10. Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.

    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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