Testing for parameter instability and structural change in persistent predictive regressions

Torben G. Andersen*, Rasmus T. Varneskov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and multiple structural changes with unknown break dates, and the number of breaks being known or unknown. We establish asymptotic limit theory for the tests, showing that it coincides with standard testing procedures. As a consequence, existing critical values for tied-down Bessel processes may be applied, without modification. We implement the new structural change tests to explore the stability of the fractionally cointegrating relation between implied- and realized volatility (IV and RV). Moreover, we assess the relative efficiency of IV forecasts against a challenging time-series benchmark constructed from high-frequency data. Unlike existing studies, we find evidence that the IV–RV cointegrating relation is unstable, and that carefully constructed time-series forecasts are more efficient than IV in capturing low-frequency movements in RV.

Original languageEnglish (US)
Pages (from-to)361-386
Number of pages26
JournalJournal of Econometrics
Issue number2
StatePublished - Dec 2022


  • Cointegration
  • Fractional integration
  • Frequency domain inference
  • Local spectrum procedure
  • Parameter instability
  • Structural change
  • Volatility forecasting

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'Testing for parameter instability and structural change in persistent predictive regressions'. Together they form a unique fingerprint.

Cite this