• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Kleibergen, F. and Van Dijk, HermanK. (1994). On the shape of the likelihood/posterior in cointegration models Econometric Theory, 10(3-4):514--551.


  • Journal
    Econometric Theory

A vector autoregressive (VAR) model is specified with equation system parameters, which directly reflect the possible cointegrating nature of the analyzed time series. By using a flat/diffuse prior, we show that the marginal posteriors of the parameters of interest (multipliers of the cointegrating vectors) may be nonintegrable and favor difference stationary models in an undesired way. To choose between stationary, cointegrated, and difference stationary models in a meaningful way, the Jeffreys prior principle is used. We investigate the sensitivity of the posterior results with respect to the construction of the Jeffreys prior. In this context, we also analyze the effect of fixed and stochastic initial values. The theoretical results are illustrated by using a VAR model for shortand long–term interest rates in the United States.