Estimating dynamic equilibrium models using mixed frequency macro and financial data

Bent Jesper Christensen, Olaf Posch*, Michel Van Der Wel

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


We provide a framework for inference in dynamic equilibrium models including financial market data at daily frequency, along with macro series at standard lower frequency. Our formulation of the macro-finance model in continuous time conveniently accounts for the difference in observation frequency. We suggest the use of martingale estimating functions (MEF) to infer the structural parameters of the model directly through a nonlinear scheme. This method is compared to regression-based methods and the generalized method of moments (GMM). We illustrate our approaches by estimating various versions of the AK-Vasicek model with mean-reverting interest rates. We provide asymptotic theory and Monte Carlo evidence on the small sample behavior of the estimators and report empirical estimates using 30 years of US macro and financial data.

Original languageEnglish
JournalJournal of Econometrics
Pages (from-to)116-137
Number of pages22
Publication statusPublished - 1 Sept 2016


  • AK-Vasicek model
  • Martingale estimating function
  • Structural estimation


Dive into the research topics of 'Estimating dynamic equilibrium models using mixed frequency macro and financial data'. Together they form a unique fingerprint.

Cite this