Empirical Economics

, Volume 58, Issue 1, pp 379–392 | Cite as

Forecasting of recessions via dynamic probit for time series: replication and extension of Kauppi and Saikkonen (2008)

  • Byeong U. Park
  • Léopold Simar
  • Valentin ZelenyukEmail author


In this work, we first replicate the results of the fully parametric dynamic probit model for forecasting US recessions from Kauppi and Saikkonen (Rev Econ Stat 90(4):777–791, 2008) [which is in the spirit of Estrella and Mishkin (Rev Econ Stat 80(1):45–61, 1998) and Dueker (Rev Fed Reserve Bank St Louis 79(2):41–51, 1997)] and then contrast them to results from a nonparametric local-likelihood dynamic choice model for the same data. We then use expanded data to gain insights on whether these models could have warned the public about approach of the latest recession, associated with the Global Financial Crisis. Finally, we also apply both approaches to gain insights for 2018.


Forecasting of recessions Nonparametric quasi-likelihood Local-likelihood Dynamic discrete choice 

JEL Classification

C14 C22 C25 E37 



We thank Adrian Pagan, colleagues and participants of various events where earlier versions of this work was presented for their valuable feedback. We acknowledge and thank for the financial support provided by the ARC Discovery Grant (DP130101022) and ARC FT170100401, from the ‘Interuniversity Attraction Pole’, Phase VII (No. P7/06) of the Belgian Science Policy, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A4A1007895). We also thank Ms. Ailin Leng for her technical assistance with some data collection at the early stage of the project. Finally, we thank the editor and two anonymous referees for the fruitful feedback that helped improving this paper substantially.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Byeong U. Park
    • 1
  • Léopold Simar
    • 2
  • Valentin Zelenyuk
    • 3
    Email author
  1. 1.Department of StatisticsSeoul National UniversitySeoulKorea
  2. 2.Institut de Statistique, Biostatistique et Sciences ActuariellesUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  3. 3.School of Economics and Centre for Efficiency and Productivity Analysis (CEPA)The University of QueenslandBrisbaneAustralia

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