Ken is an Assistant Professor of Statistical Science at Temple University, Fox School of Business. Before Temple, he was a Senior Research Professional in Econometrics and Statistics at The University of Chicago, Booth School of Business. He received his Ph.D. in statistical science at Duke University in the Department of Statistical Science and his Ph.D. in Economics at Keio University in the Department of Economics. He also has a M.S. in statistical science from Duke University and a dual masters from Keio University (Economics) and L’Institute D’Etudes Politiques De Paris (Economics and Public Policy, joint with Ecole Polytechnique and ENSAE). As an undergraduate at Keio University, he received a B.A. in Economics with a focus on Bayesian econometrics and film theory.
Research interests include: Bayesian statistics, time series, forecast combination/model averaging/ensemble learning, decision theory, forecasting, decision making under uncertainty, econometrics, causal inference, non-linear and latent structures, on-line filtering, and parallel/GPU computing, among others.
- Ph.D., Statistical Science Duke University, Durham, North Carolina
- Ph.D., Economics Keio University, Tokyo, Japan
- M.S., Statistical Science Duke University, Durham, North Carolina
- M.A., Economics Keio University, Tokyo, Japan
- Master of Economics and Public Policy L’Institut d’Etudes Politiques de Paris, Paris, France
- B.A., Economics Keio University, Tokyo, Japan
- 2019 – present, Temple University, Philadelphia, Pennsylvania Fox School of Business, Department of Statistical Science Assistant Professor of Statistical Science
- 2017-2019, University of Chicago, Chicago, Illinois Booth School of Business, Econometrics and Statistics Senior Research Professional/Post-Doc
- McAlinn, K., Aastveit, K.A., Nakajima, J., & West, M. “Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting” 2019 Journal of the American Statistical Association (in press)
- McAlinn, K. & West, M. “Dynamic Bayesian Predictive Synthesis in Time Series Forecasting” 2019 Journal of Econometrics 210: 155-169