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

• Issue Information

  Journal of Applied Econometrics · 2023-11-01

  paratextOpen access

  No abstract is available for this article.

  PublisherOA PDFDOI

• Rényi Divergence and Monte Carlo Integration

  Oxford University Press eBooks · 2020

  1st authorCorresponding
  • Computer Science
  • Data Mining
  • Computer Science

  Abstract Rényi divergence is a natural way to measure the rate of information flow in contexts like Bayesian updating. This chapter shows how Monte Carlo integration can be used to measure Rényi divergence when (as is often the case) only kernels of the relevant probability densities are available. The chapter further demonstrates that Rényi divergence is central to the convergence and efficiency of Monte Carlo integration procedures in which information flow is controlled. It uses this perspective to develop more flexible approaches to the controlled introduction of information; in the limited set of examples considered here, these alternatives enhance efficiency.

  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 problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.

  PublisherDOI

• Bayesian A/B Inference

  2019-09-27

  book-chapter1st authorCorresponding

  Abstract Bayesian A/B inference (BABI) is a method that combines subjective prior information with data from A/B experiments to provide inference for lift – the difference in a measure of response in control and treatment, expressed as its ratio to the measure of response in control. The procedure is embedded in stable code that can be executed in a few seconds for an experiment, regardless of sample size, and caters to the objectives and technical background of the owners of experiments. BABI provides more powerful tests of the hypothesis of the impact of treatment on lift, and sharper conclusions about the value of lift, than do legacy conventional methods. In application to 21 large online experiments, the credible interval is 60% to 65% shorter than the conventional confidence interval in the median case, and by close to 100% in a significant proportion of cases; in rare cases, BABI credible intervals are longer than conventional confidence intervals and then by no more than about 10%.

  PublisherDOI

• Endogeneity and Exogeneity

  The New Palgrave Dictionary of Economics · 2018-01-01

  book-chapter1st authorCorresponding

  Endogeneity and exogeneity are properties of variables in economic or econometric models. The specification of these properties in variables is an essential component of the process of model specification. This article considers their application in the specification of, in turn, deterministic and stochastic models.

  PublisherDOI

• Econometrics

  The New Palgrave Dictionary of Economics · 2018-01-01

  book-chapter1st authorCorresponding

  As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly. Major advances have taken place in the analysis of cross-sectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take it into account either by integrating out its effects or by modelling the sources of heterogeneity when suitable panel data exist. The counterfactual considerations that underlie policy analysis and treatment valuation have been given a more satisfactory foundation. New time-series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Nonlinear econometric techniques are used increasingly in the analysis of cross-section and time-series observations. Applications of Bayesian techniques to econometric problems have been promoted largely by advances in computer power and computational techniques. The use of Bayesian techniques has in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process, thus providing a basis for 'real time econometrics'.

  PublisherDOI

• Sequentially adaptive Bayesian learning algorithms for inference and optimization

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

  article1st authorCorresponding
  PublisherDOI

• 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 pool.

  PublisherDOI

• Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP

  Econometrics · 2016-03-02 · 3 citations

  articleOpen access1st authorCorresponding

  There is a one-to-one mapping between the conventional time series parameters of a third-order autoregression and the more interpretable parameters of secular half-life, cyclical half-life and cycle period. The latter parameterization is better suited to interpretation of results using both Bayesian and maximum likelihood methods and to expression of a substantive prior distribution using Bayesian methods. The paper demonstrates how to approach both problems using the sequentially adaptive Bayesian learning algorithm and sequentially adaptive Bayesian learning algorithm (SABL) software, which eliminates virtually of the substantial technical overhead required in conventional approaches and produces results quickly and reliably. The work utilizes methodological innovations in SABL including optimization of irregular and multimodal functions and production of the conventional maximum likelihood asymptotic variance matrix as a by-product.

  PublisherOA PDFDOI

• AG_REStat_code_data_readme_file_2016_10_20.pdf

  Harvard Dataverse · 2016-01-01

  datasetOpen accessSenior author

  :unav

  PublisherDOI

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