Authors Title Year Journal/Proceedings/Book
McAlinn, K. & Takanashi, K.

Mean-shift least squares model averaging

[arXiv]

2019 arXiv:1912.01194
Takanashi, K. & McAlinn, K.

Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis

[arXiv]

2019 arXiv:1911.08662
McAlinn, K., Aastveit, K.A., Nakajima, J., & West, M.

Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

[URL] [arXiv] [Code]

2019 Journal of the American Statistical Association (in press)
McAlinn, K., Ushio, A., & Nakatsuma, T.

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

[arXiv]

2019 Journal of Forecasting (in press)
McAlinn, K. & West, M.

Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

[URL] [arXiv] [Code]

2019

Journal of Econometrics

210: 155-169  

McAlinn, K., Rockova, V., & Saha, E.

Dynamic sparse factor analysis

[arXiv]

2018 arXiv:1812.04187
McAlinn, K., Aastveit, K.A., & West, M.

Comment on "Using stacking to average Bayesian predictive distributions"

[URL]

2018

Bayesian Analysis

13: 917-1003

Bianchi, D. & McAlinn, K.

Large-Scale Dynamic Predictive Regressions

[arXiv] [SSRN]

2018 arXiv:1803.06738
McAlinn, K.

Dynamic Mixed Frequency Synthesis for Economic Nowcasting

[arXiv]

2017 arXiv:1712.03646
Rockova, V. & McAlinn, K.

Dynamic Variable Selection with Spike-and-Slab Process Priors

[arXiv]

2017

Invited revision: Bayesian Analysis

arXiv:1708.00085

McAlinn, K.

Dynamic Modeling and Bayesian Predictive Synthesis

[URL]

2017 Doctoral Thesis
McAlinn, K. & Nakatsuma, T.

Fully Parallel Particle Learning for GPGPUs and Other Parallel Devices

[arXiv]

2015 arXiv:1212.1639
Katsura, H. & McAlinn, K.

Stochastic volatility models and its application to VIX derivatives (in Japanese)

[URL]

2014 JAFEE Journal

© Kenichiro McAlinn, 2016.