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| Author | : Chew Lian Chua |
| Publisher | : |
| Total Pages | : 356 |
| Release | : 2003 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
| Author | : David Fletcher |
| Publisher | : Springer |
| Total Pages | : 107 |
| Release | : 2019-01-17 |
| Genre | : Mathematics |
| ISBN | : 3662585413 |
This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
| Author | : Siddhartha Chib |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 656 |
| Release | : 2008-12-18 |
| Genre | : Business & Economics |
| ISBN | : 1848553099 |
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
| Author | : Huigang Chen |
| Publisher | : International Monetary Fund |
| Total Pages | : 47 |
| Release | : 2011-10-01 |
| Genre | : Business & Economics |
| ISBN | : 1463921306 |
This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.
| Author | : Ivan Jeliazkov |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 361 |
| Release | : 2014-11-21 |
| Genre | : Political Science |
| ISBN | : 1784411841 |
This volume of Advances in Econometrics 34 focusses on Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research.
| Author | : Martin Feldkircher |
| Publisher | : International Monetary Fund |
| Total Pages | : 42 |
| Release | : 2009-09-01 |
| Genre | : Business & Economics |
| ISBN | : 1451917716 |
Default prior choices fixing Zellner''s g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts posterior model distributions to data quality. Analytically, existing work on the hyper-g-prior is complemented by posterior expressions essential to fully Bayesian analysis and to sound numerical implementation. A simulation experiment illustrates the implications for posterior inference. Furthermore, an application to determinants of economic growth identifies several covariates whose robustness differs considerably from previous results.
| Author | : Gerda Claeskens |
| Publisher | : |
| Total Pages | : 312 |
| Release | : 2008-07-28 |
| Genre | : Mathematics |
| ISBN | : 9780521852258 |
First book to synthesize the research and practice from the active field of model selection.
| Author | : Donald A. Berry |
| Publisher | : John Wiley & Sons |
| Total Pages | : 610 |
| Release | : 1996 |
| Genre | : Business & Economics |
| ISBN | : 9780471118565 |
This book is a definitive work that captures the current state of knowledge of Bayesian Analysis in Statistics and Econometrics and attempts to move it forward. It covers such topics as foundations, forecasting inferential matters, regression, computation and applications.
| Author | : John Geweke |
| Publisher | : Oxford University Press, USA |
| Total Pages | : 571 |
| Release | : 2011-09-29 |
| Genre | : Business & Economics |
| ISBN | : 0199559082 |
A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
| Author | : Luc Bauwens |
| Publisher | : OUP Oxford |
| Total Pages | : 370 |
| Release | : 2000-01-06 |
| Genre | : Business & Economics |
| ISBN | : 0191588466 |
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
