Associate Professor Xin Jin has his paper accepted for publication by Journal of Econometrics, a first-tier journal as listed by SUFE. The paper entitled “Infinite Markov Pooling of Predictive Distributions”was co-authored with John Maheu from McMaster University and Qiao Yang from ShanghaiTech University. He has two papers published on the same journal before.
This paper introduces novel approaches to forecast pooling methods based on a nonparametric prior for a weight vector combining predictive densities. The first approach places a Dirichlet process prior on the weight vector and generalizes the static linear pool. The second approach uses a hierarchical Dirichlet process prior to allow the weight vector to follow an infinite hidden Markov chain. This generalizes dynamic prediction pools to the nonparametric setting. Efficient posterior simulation based on MCMC methods is also examined. Detailed applications to short-term interest rates, realized covariance matrices and asset pricing models demonstrate that the nonparametric pool forecasts well. The paper concludes with extensions and an application for calibrating and combining predictive densities.
Associate Professor Xin Jin received his Ph.D. in 2012 from University of Toronto and joined our school as a tenure-track faculty member. His research interests include applied econometrics and financial econometrics.