Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no‐arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no‐change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no‐arbitrage affine term structure model's requirements that yields obey a factor structure and that the factors follow a Markov process. In the term structure model, its cross‐equation no‐arbitrage restrictions on the factor loadings appear to play a marginal role in forecasting gains.