John Geweke

About

John Geweke joins the University of Washington Department of Economics as an affiliate professor. He is known for his contributions to econometric theory in time series analysis and Bayesian modeling. Geweke is a Fellow of the Econometric Society and the American Statistical Association, and has served as co-editor of the Journal of Econometrics, the Journal of Applied Econometrics, and editor of the Journal of Business and Economic Statistics. His most recent book is 'Complete and Incomplete Econometric Models' (Princeton UP, 2010). Geweke's academic career includes positions at the University of Iowa, the University of Minnesota, Duke University, Carnegie-Mellon University, and the University of Wisconsin-Madison. He completed his Ph.D. in economics at the University of Minnesota in 1975. Additionally, he is a senior economist at Amazon.

Research topics

• Data Mining
• Artificial Intelligence
• Computer Science
• Statistical physics
• Physics
• Mathematics
• Mathematical optimization
• Statistics

Selected publications

• Prediction Using Several Macroeconomic Models

  The Review of Economics and Statistics · 2017-01-18 · 54 citations

  articleSenior author

  We establish methods that improve the predictions of macroeconometric models—dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions—using a quarterly U.S. data set. We measure prediction quality with one-step-ahead probability densities assigned in real time. Two steps lead to substantial improvements: (a) the use of full Bayesian predictive distributions rather than conditioning on the posterior mode for parameters and (b) the use of an equally weighted…

  PublisherDOI

• Bayesian Inference for ARFIMA Models

  Journal of Time Series Analysis · 2019-01-02 · 13 citations

  articleCorresponding

  This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several pro…

  PublisherDOI

• Sequentially adaptive Bayesian learning algorithms for inference and optimization

  Journal of Econometrics · 2018-11-12 · 10 citations

  article1st authorCorresponding
  PublisherDOI

• \Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options"

  2014-01-01 · 9 citations

  article
  Publisher

• Issue Information

  Journal of Applied Econometrics · 2023-11-01

  paratextOpen access

  No abstract is available for this article.

  PublisherOA PDFDOI

Recent grants

• Smoothly Mixing Regression Models

  NSF · $210k · 2007–2010

Frequent coauthors

• Gautam Gowrisankaran

  Centre for Economic Policy Research

  61 shared

• Robert Town

  55 shared

• Gianni Amisano

  Bank of Finland

  52 shared

• Michael P. Keane

  34 shared

• Preston J. Miller

  28 shared

• Daniel M. Chin

  27 shared

• Garland Durham

  21 shared

• Robert L. Ohsfeldt

  17 shared

Education

• Ph.D., Economics

  University of Minnesota

  1975

Awards & honors

• Fellow of the Econometric Society
• Fellow of the American Statistical Association

Similar researchers at University of Washington

• Stephen Turnovskyh-67
• Fabio Ghironih-53
• Xu Tanh-45