Contemporary Bayesian Econometrics and Statistics

2006 ◽  
Vol 101 (475) ◽  
pp. 1313-1314
Author(s):  
Thomas Otter
Keyword(s):  
Bayesian Econometrics

Introduction to Bayesian Econometrics

2009 ◽  
Vol 85 (270) ◽  
pp. 364-366
Author(s):  
Liana Jacobi
Keyword(s):  
Bayesian Econometrics

The Oxford Handbook of Bayesian Econometrics

2011 ◽  
Keyword(s):  
Bayesian Methods ◽  
Particle Filtering ◽  
State Of The Art ◽  
State Space Models ◽  
Bayesian Econometrics ◽  
Econometric Methods ◽  
Econometric Methodology ◽  
Posterior Simulation ◽  
Nonparametric Techniques ◽  
Bayesian Time Series

Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. The Oxford Handbook of Bayesian Econometrics is a single source about Bayesian methods in specialized fields. It contains articles by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with articles on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes articles on Bayesian principles and methodology.

Bayesian Methods In Finance

2011 ◽  
pp. 438-512 ◽  
Author(s):  
Eric Jacquier ◽  
Nicholas Polson
Keyword(s):  
Financial Markets ◽  
Bayesian Methods ◽  
Particle Filtering ◽  
Optimal Allocation ◽  
Financial Econometrics ◽  
Bayesian Econometrics ◽  
Empirical Asset Pricing ◽  
Mcmc Algorithms ◽  
Volatility Models ◽  
The Mean

This article looks at the usefulness of Bayesian methods in finance. It covers all the major topics in finance. It discusses the predictability of the mean of asset returns, central to finance, as it relates to the efficiency of financial markets. It reviews the economic relevance of predictability and its impact on optimal allocation. It also describes the Markov chain Monte Carlo (MCMC) and particle filtering algorithms that are important in modern Bayesian financial econometrics. MCMC algorithms have resulted in a tremendous growth in the use of stochastic volatility models in financial econometrics. This article also contains some major contributions of Bayesian econometrics to the literature on empirical asset pricing. Many of the other themes in modern Bayesian econometrics, including the use of shrinkage and the interaction between theory and econometrics are discussed. This article ends up with the discussion of a promising recent development in finance: filtering with parameter learning.

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