Simula@BI seminar: James Duffy

Simula@BI invites Professor James Duffy, Oxford University, to give a talk titled "Cointegration with Occasionally Binding Constraints" within the field of econometrics. This is a joint work with S. Mavroeidis and S. Wycherley.

  • Starts:12:00, 6 February 2024
  • Ends:13:00, 6 February 2024
  • Location:BI - campus Oslo: B3 inner area - next to meeting room B3i-108 or Zoom
  • Contact:Siri Johnsen (siri.johnsen@bi.no)

Simula@BI invites Professor James Duffy, Oxford University, to give a talk within the field of econometrics.


In the literature on nonlinear cointegration, a long-standing open problem relates to how a (nonlinear) vector autoregression, which provides a unified description of the short- and long-run dynamics of a collection of time series, can generate 'nonlinear cointegration' in the profound sense of those series sharing common nonlinear stochastic trends. We consider this problem in the setting of the censored and kinked structural VAR (CKSVAR), which provides a flexible yet tractable framework within which to model time series that are subject to threshold-type nonlinearities, such as those arising due to occasionally binding constraints, of which the zero lower bound (ZLB) on short-term nominal interest rates provides a leading example. We provide a complete characterisation of how common linear and nonlinear stochastic trends may be generated in this model, via unit roots and appropriate generalisations of the usual rank conditions, providing the first extension to date of the Granger-Johansen representation theorem to a nonlinearly cointegrated setting, and thereby giving the first successful treatment of the open problem. The limiting common trend processes include regulated, censored and kinked Brownian motions, none of which have previously appeared in the literature on cointegrated VARs. Our results and running examples illustrate that the CKSVAR is capable of supporting a far richer variety of long-run behaviour than is a linear VAR, in ways that may be particularly useful for the identification of structural parameters.