Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Global recessions and booms: what do Probit models tell us?

1 European Central Bank, Sonnemannstrasse 20, 60314 Frankfurt am Main, Germany
2 Weiden Technical University of Applied Sciences, Weiden, Germany

We present non-linear binary Probit models to capture the turning points in global economic activity as well as in advanced and emerging economies from 1980 to 2016. For that purpose, we use four different business cycle dating methods to identify the regimes (upswings, downswings). We find that especially activity-driven variables are important indicators for the turning points. Moreover, we identify similarities and differences between the different regions in this respect.
  Figure/Table
  Supplementary
  Article Metrics

Keywords Global GDP; Probit; turning points

Citation: Ursel Baumann, Ramón Gómez Salvador, Franz Seitz. Global recessions and booms: what do Probit models tell us?. Quantitative Finance and Economics, 2019, 3(1): 187-200. doi: 10.3934/QFE.2019.1.187

References

  • 1.Abberger K, Nierhaus W (2010) Markov-switching and the Ifo business climate. J Bus Cycle Measure Anal, 1–13.
  • 2. Baumann U, Gómez Salvador R , Seitz F (2019) Detecting turning points in global economic activity. European Central Bank, Working paper.
  • 3. Boysen-Hogrefe J (2012) A note on prediction recessions in the euro area using real M1. Econ Bull 32: 1291–1301.
  • 4. Bry G, Boschan C (1971) Programmed selection of cyclical turning points, in: Bry, G. & C. Boschan (eds.), Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, UMI, 7–63.
  • 5. Camacho M, Martinez-Martin J (2015) Monitoring the world business cycle. Econ Model 51: 617–625.    
  • 6. Chauvet M, Potter S (2010) Business Cycle Monitoring with Structural Change. Inter J Forecast 6: 777–793.
  • 7. Christiansen C, Eriksen JN, Møller SV (2014) Forecasting US recessions: The role of sentiment. J Bank Financ 49: 459–468.    
  • 8. Ferrara L, Marsilli C (2014) Nowcasting global economic growth: A factor-augmented mixed-frequency approach. Banque de France, Working Paper No. 515.
  • 9. Fornari F, Lemke W (2010) Predicting recession probabilities with financial variables. ECB Working Paper Series NO 1255.
  • 10. Fossati S (2015) Forecasting US recessions with macro factors. Appl Econ 47: 5726–5738.    
  • 11. Haltmaier J (2008) Predicting cycles in economic activity, Board of Governors of the Federal Reserve System. International Finance Discussion Papers Number 926.
  • 12. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57: 357–384.    
  • 13. Harding D (2008) Detecting and forecasting business cycle turning points. MPRA Paper No. 33583.
  • 14. Harding D, Pagan A (2002) Dissecting the cycle: A methodological investigation. J Monetary Econ 49: 365–381.    
  • 15. Hsu T (2016) U.S. recession forecasting using probit models with asset index predictor variables, The University of Maryland Baltimore County.
  • 16. Layton AP, Katsuura M (2001) Comparison of regime switching, probit and logit models in dating and forecasting US business cycles. Int J Forecast 17: 403–417.    
  • 17. Nyberg H (2014) A bivariate autoregressive probit model: Business cycle linkages and transmission of recession probabilities. Macroecon Dyn 18: 838–862.    
  • 18. Proaño CR (2017) Detecting and predicting economic accelerations, recessions, and normal growth periods in real-time. J Forecast 36: 26–42.    
  • 19. Ravazzolo F, Vespignani JL (2015) A new monthly indicator of global real economic activity, Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute. Working Paper No. 244
  • 20. Stratford K (2013) Nowcasting world GDP and trade using global indicators. Bank Engl Q Bull Q3: 233–243
  • 21. Verbeek M (2012) A guide to modern econometrics, 4th ed., Wiley.

 

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved