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Identification and real-time forecasting of Norwegian business cycles

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  • Aastveit, Knut Are
  • Jore, Anne Sofie
  • Ravazzolo, Francesco

Abstract

We define and forecast classical business cycle turning points for the Norwegian economy. When defining reference business cycles, we compare a univariate and a multivariate Bry–Boschan approach with univariate Markov-switching models and Markov-switching factor models. On the basis of a receiver operating characteristic curve methodology and a comparison of the business cycle turning points of Norway’s main trading partners, we find that a Markov-switching factor model provides the most reasonable definition of Norwegian business cycles for the sample 1978Q1–2011Q4. In a real-time out-of-sample forecasting exercise, focusing on the last recession, we show that univariate Markov-switching models applied to surveys and a financial conditions index are timely and accurate in calling the last peak in real time. However, the models are less accurate and timely in calling the trough in real time.

Suggested Citation

  • Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
  • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:283-292
    DOI: 10.1016/j.ijforecast.2015.06.006
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    2. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    3. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    4. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org.
    5. Komla M. Agudze & Monica Billio & Roberto Casarin & Francesco Ravazzolo, 2021. "Markov Switching Panel with Endogenous Synchronization Effects," BEMPS - Bozen Economics & Management Paper Series BEMPS82, Faculty of Economics and Management at the Free University of Bozen.
    6. Elena Deryugina & Alexey Ponomarenko, 2017. "Real-time determination of credit cycle phases in emerging markets," Bank of Russia Working Paper Series wps17, Bank of Russia.
    7. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    8. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    9. Brubakk, Leif & ter Ellen, Saskia & Robstad, Ørjan & Xu, Hong, 2019. "The macroeconomic effects of forward communication," Working Paper 2019/20, Norges Bank.
    10. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, November.
    11. Elena Deryugina & Alexey Ponomarenko, 2019. "Determination of the Current Phase of the Credit Cycle in Emerging Markets," Russian Journal of Money and Finance, Bank of Russia, vol. 78(2), pages 28-42, June.

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    More about this item

    Keywords

    Business cycle; Dating rules; Turning points; Real-time data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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