Kenichiro Mcalinn

Profile Picture of Kenichiro Mcalinn

Kenichiro Mcalinn

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Assistant Professor

Biography

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.

Research Interests

  • Economics
  • Finance
  • Marketing
  • Business

Courses Taught

Number

Name

Level

STAT 3503

Applied Statistics and Data Science

Undergraduate

STAT 8003

Statistical Methods and Concepts

Graduate

STAT 8004

Statistical Modeling and Inference

Graduate

STAT 8109

Applied Statistics and Data Science

Graduate

STAT 8982

Independent Study

Graduate

Selected Publications

Recent

  • McAlinn, K. (2021). Mixed-frequency Bayesian predictive synthesis for economic nowcasting. Journal of the Royal Statistical Society. Series C: Applied Statistics, 70(5), 1143-1163. doi: 10.1111/rssc.12500.

  • McAlinn, K., Aastveit, K., Nakajima, J., & West, M. (2020). Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting. Journal of the American Statistical Association, 115(531), 1092-1110. doi: 10.1080/01621459.2019.1660171.

  • McAlinn, K., Ushio, A., & Nakatsuma, T. (2020). Volatility forecasts using stochastic volatility models with nonlinear leverage effects. Journal of Forecasting, 39(2), 143-154. doi: 10.1002/for.2618.

  • Rockova, V. & McAlinn, K. (2020). Dynamic Variable Selection with Spike-and-Slab Process Priors. Bayesian Analysis, 16(1), 233-269. doi: 10.1214/20-BA1199.

  • McAlinn, K. & West, M. (2019). Dynamic Bayesian predictive synthesis in time series forecasting. Journal of Econometrics, 210(1), 155-169. doi: 10.1016/j.jeconom.2018.11.010.