Dale J. Poirier

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Current working papers

Bayesian Interpretations of Heteroskedastic Consistent Covariance Estimators Using the Informed Bayesian Bootstrap

by Dale Poirier

Abstract: This paper provides Bayesian rationalizations for White’s heteroskedastic consistent (HC) covariance estimator and various modifications of it. An informed Bayesian bootstrap provides the statistical framework.

Dynamic and Structural Features of Intifada Violence: A Markov Process Approach

by Dale Poirier and Ivan Jeliazkov

Abstract: This paper analyzes the daily incidence of violence during the Second Intifada. We compare several alternative statistical models with different dynamic and structural stability characteristics while keeping modelling complexity to a minimum by only maintaining the assumption that the process under consideration is at most a second order discrete Markov process. For the pooled data, the best model is one with asymmetric dynamics, where one Israeli and two Palestinian lags determine the conditional probability of violence. However, when we allow for structural change, the evidence strongly favors the hypothesis of structural instability across political regime sub-periods, within which dynamics are generally weak

Abstracts of selected publications

Paper 1 “Bayesian Econometric Methods” (with Gary Koop and Justin Tobias), in Econometrics Exercises Series, Vol. 7, K. Abadir, J. Magnus, and P. C. B. Phillips, eds. (Cambridge: Cambridge University Press, 2007).

Abstract: This book is a volume in the Econometric Exercises series. It teaches principles of Bayesian econometrics by posing a series of theoretical and applied questions, and providing detailed solutions to those questions. This text is primarily suitable for graduate study in econometrics, though it can be used for advanced undergraduate courses, and should generate interest from students in related fields, including finance, marketing, agricultural economics, business economics, and other disciplines that employ statistical methods. The book provides a detailed treatment of a wide array of models commonly employed by economists and statisticians, including linear regression-based models, hierarchical models, latent variable models, mixture models and time series models. Basics of random variable generation and simulation via Markov Chain Monte Carlo (MCMC) methods are also provided. Finally, posterior simulators for each type of model are rigorously derived, and MATLAB computer programs for fitting these models (using both actual and generated data sets) are provided on the web site accompanying the text.

Paper 2 “The Growth of Bayesian Methods in Statistics and Economics Since 1970,” Bayesian Analysis, Vol. 1 (No. 4, 2006), 969-980

Abstract: To measure the impact of Bayesian reasoning, this paper investigates the occurrence of two words, “Bayes” and “Bayesian,” since 1970 in journal articles in a variety of disciplines, with a focus on economics and statistics. The growth in statistics is documented, but the growth in economics is largely confined to economic theory/mathematical economics rather than econometrics.


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