On The Effect Of Prior Assumptions In Bayesian Model Averaging With Applications To Growth Regression PDF Download
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| Author | : Eduardo Ley |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 2007 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
Download On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression Book in PDF, ePub and Kindle
This paper examines the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. The paper analyzes the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors, and predictive performance. The analysis illustrates these issues in the context of cross-country growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. The results favor particular prior structures for use in this and related contexts.
| Author | : Eduardo Ley |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Download Mixtures of G-priors for Bayesian Model Averaging with Economic Application Book in PDF, ePub and Kindle
| Author | : Stefan Zeugner |
| Publisher | : International Monetary Fund |
| Total Pages | : 41 |
| Release | : 2009-09-01 |
| Genre | : Business & Economics |
| ISBN | : 1451873492 |
Download Benchmark Priors Revisited Book in PDF, ePub and Kindle
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 | : Huigang Chen |
| Publisher | : International Monetary Fund |
| Total Pages | : 47 |
| Release | : 2011-10-01 |
| Genre | : Business & Economics |
| ISBN | : 1463921306 |
Download Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model Book in PDF, ePub and Kindle
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 | : J.R Magnus |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
Download Concept-Based Bayesian Model Averaging and Growth Empirics Book in PDF, ePub and Kindle
In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth theories taking into account the measurement problem in the growth regression. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques when the number of variables is large or when computing time is limited, and we propose possible strategies for sensitivity analysis.
| Author | : Gernot Doppelhofer |
| Publisher | : |
| Total Pages | : 66 |
| Release | : 2000 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
Download Determinants of Long-term Growth Book in PDF, ePub and Kindle
This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combination of included variables. The weights applied to individual regressions are justified on Bayesian grounds in a way similar to the well-known Schwarz criterion. Of 32 explanatory variables we find 11 to be robustly partially correlated with long-term growth and another five variables to be marginally related. Of all the variables considered, the strongest evidence is for the initial level of real GDP per capita.
| Author | : Andrew Gelman |
| Publisher | : CRC Press |
| Total Pages | : 677 |
| Release | : 2013-11-01 |
| Genre | : Mathematics |
| ISBN | : 1439840954 |
Download Bayesian Data Analysis, Third Edition Book in PDF, ePub and Kindle
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
| Author | : David Fletcher |
| Publisher | : Springer |
| Total Pages | : 107 |
| Release | : 2019-01-17 |
| Genre | : Mathematics |
| ISBN | : 3662585413 |
Download Model Averaging Book in PDF, ePub and Kindle
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 | : José M. Bernardo |
| Publisher | : Oxford University Press |
| Total Pages | : 717 |
| Release | : 2011-10-06 |
| Genre | : Mathematics |
| ISBN | : 0199694583 |
Download Bayesian Statistics 9 Book in PDF, ePub and Kindle
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
| Author | : N. Thompson Hobbs |
| Publisher | : Princeton University Press |
| Total Pages | : 315 |
| Release | : 2015-08-04 |
| Genre | : Science |
| ISBN | : 1400866553 |
Download Bayesian Models Book in PDF, ePub and Kindle
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models
