Forecasting government bond yields with large Bayesian vector autoregressions

@inproceedings{Carriero2015ForecastingGB,
  title={Forecasting government bond yields with large Bayesian vector autoregressions},
  author={Andrea Carriero and George Kapetanios and Massimiliano Marcellino},
  year={2015}
}
We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing on the US, we provide an extensive study on the forecasting performance of the proposed model relative… CONTINUE READING