Nicholas Polson is a Bayesian statistician who conducts research on Financial Econometrics, Markov chain Monte Carlo, Particle learning, and Bayesian inference. Inspired by an interest in probability, Polson has developed a number of new algorithms and applied them to the fields of statistics and financial econometrics, including the Bayesian analysis of stochastic volatility models and sequential particle learning for statistical inference.
Polson’s article, “Bayesian Analysis of Stochastic Volatility Models,” was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics. His recent work includes methods for sparse Bayesian estimation with application to high dimensional regression and classification.
2013 - 2014 Course Schedule
||Econometrics and Statistics Colloquium
||Probability and Statistics
With M. Johannes, "MCMC Methods for Financial Econometrics," Handbook of Financial Econonmetrics (2004).
With B. Eraker and M. Johannes, "The Impact of Jumps in Volatility in Returns," Journal of Finance (2003).
With E. Jacquier and P. Rossi, "Bayesian Analysis of Stochastic Volatility Models," Journal of Business and Economic Statistics (1994, 2002).
Invited paper with discussion, "Convergence of Markov Chain Monte Carlo Algorithms," Fifth Valencia Meeting on Bayesian Statistics.
For a listing of research publications please visit
’s university library listing