Research

My main research areas are firmly within the realm of Bayesian statistics and Markov chain Monte Carlo methods. I’m generally interested in dynamic models as well as MCMC and other computing methods for dynamic models. I also have in interest in constructing priors for covariance matrices, though this is partially motivated by my interest in dynamic models. Finally, I’m also interested in applications of Bayesian statistics to time series problems and causal inference, particularly in economics, and applications to experimental economics. You can find a list of my papers below.

  • [Under review] Interweaving Markov Chain Monte Carlo Strategies for Efficient Estimation of Dynamic Linear Models. (Matthew Simpson, Jarad Niemi and Vivekananda Roy)

  • [2014] Covariance Matrix Prior Distributions for Hierarchical Linear Models, Kansas State University Conference on Applied Statistics in Agriculture. (Ignacio Alvarez-Castro, Matthew Simpson, and Jarad Niemi)

  • [Working paper] The Matrix-F Prior for Covariance Matrices. (Matthew Simpson, Jarad Niemi, Vivekananda Roy, and Alicia Carriquiry)

  • [Working paper] A Bayesian Analysis of the National School Lunch Program. (Matthew Simpson and Brent Kreider)