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Robust Bayesian Inference For Econometrics
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Book Synopsis Robust Bayesian Inference for Econometrics by : Raffaella Giacomini
Download or read book Robust Bayesian Inference for Econometrics written by Raffaella Giacomini and published by . This book was released on 2021 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for set-identified structural vector autoregressions, including details about numerical implementation and an empirical illustration.
Book Synopsis Robust Bayesian Analysis for Econometrics by : Raffaella Giacomini
Download or read book Robust Bayesian Analysis for Econometrics written by Raffaella Giacomini and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for setidentified structural vector autoregressions, including details about numerical implementation and an empirical illustration.
Book Synopsis Robust Bayesian Analysis by : David Rios Insua
Download or read book Robust Bayesian Analysis written by David Rios Insua and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con cerns foundational aspects and describes decision-theoretical axiomatisa tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.
Book Synopsis Robustness of Bayesian Analyses by : Joseph B. Kadane
Download or read book Robustness of Bayesian Analyses written by Joseph B. Kadane and published by North Holland. This book was released on 1984 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Bayesian Inference when the Data Conflicts with the Prior by : Thomas William Lucas
Download or read book Robust Bayesian Inference when the Data Conflicts with the Prior written by Thomas William Lucas and published by . This book was released on 1991 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Bayesian Inference on Scale Parameters by : Carmen Fernández
Download or read book Robust Bayesian Inference on Scale Parameters written by Carmen Fernández and published by . This book was released on 1996 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference by : Nicholas G. Polson
Download or read book Bayesian Inference written by Nicholas G. Polson and published by Edward Elgar Publishing. This book was released on 1995 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian econometrics.
Book Synopsis Econometrics of Structural Change by : Walter Krämer
Download or read book Econometrics of Structural Change written by Walter Krämer and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometric models are made up of assumptions which never exactly match reality. Among the most contested ones is the requirement that the coefficients of an econometric model remain stable over time. Recent years have therefore seen numerous attempts to test for it or to model possible structural change when it can no longer be ignored. This collection of papers from Empirical Economics mirrors part of this development. The point of departure of most studies in this volume is the standard linear regression model Yt = x;fJt + U (t = I, ... , 1), t where notation is obvious and where the index t emphasises the fact that structural change is mostly discussed and encountered in a time series context. It is much less of a problem for cross section data, although many tests apply there as well. The null hypothesis of most tests for structural change is that fJt = fJo for all t, i.e. that the same regression applies to all time periods in the sample and that the disturbances u are well behaved. The well known Chow test for instance assumes t that there is a single structural shift at a known point in time, i.e. that fJt = fJo (t
Book Synopsis Robust Bayesian Inference for Set-identified Models by : Raffaella Giacomini
Download or read book Robust Bayesian Inference for Set-identified Models written by Raffaella Giacomini and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set-identified models by adopting a multiple-prior (robust) Bayesian approach. We propose new tools for Bayesian inference in set-identified models and show that they have a well-defined posterior interpretation infinite samples and are asymptotically valid from the frequentist perspective. The main idea is to construct a prior class that removes the source of the disagreement: the need to specify an unrevisable prior for the structural parameter given the reduced-form parameter. The corresponding class of posteriors can be summarized by reporting the 'posterior lower and upper probabilities' of a given event and/or the 'set of posterior means' and the associated 'robust credible region'. We show that the set of posterior means is a consistent estimator of the true identi?ed set and the robust credible region has the correct frequentist asymptotic coverage for the true identified set if it is convex. Otherwise, the method provides posterior inference about the convex hull of the identified set. For impulse-response analysis in set-identified Structural Vector Autoregressions, the new tools can be used to overcome or quantify the sensitivity of standard Bayesian inference to the choice of an unrevisable prior.
Book Synopsis Robust Bayesian Inference in Empirical Regression Models by : Jacek Osiewalski
Download or read book Robust Bayesian Inference in Empirical Regression Models written by Jacek Osiewalski and published by . This book was released on 1991 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Bayesian Inference in Elliptical Regression Models by : Jacek Osiewalski
Download or read book Robust Bayesian Inference in Elliptical Regression Models written by Jacek Osiewalski and published by . This book was released on 1990 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference in Dynamic Econometric Models by : Luc Bauwens
Download or read book Bayesian Inference in Dynamic Econometric Models written by Luc Bauwens and published by OUP Oxford. This book was released on 2000-01-06 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Robustness in Econometrics by : Vladik Kreinovich
Download or read book Robustness in Econometrics written by Vladik Kreinovich and published by Springer. This book was released on 2017-02-11 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
Book Synopsis Contemporary Bayesian Econometrics and Statistics by : John Geweke
Download or read book Contemporary Bayesian Econometrics and Statistics written by John Geweke and published by John Wiley & Sons. This book was released on 2005-10-03 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
Book Synopsis Simulation-based Inference in Econometrics by : Roberto Mariano
Download or read book Simulation-based Inference in Econometrics written by Roberto Mariano and published by Cambridge University Press. This book was released on 2000-07-20 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
Book Synopsis Robust Bayesian Inference in Proxy SVARs by : Raffaella Giacomini
Download or read book Robust Bayesian Inference in Proxy SVARs written by Raffaella Giacomini and published by . This book was released on 2020 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or 'proxy SVARs'. Set-identification in these models typically occurs when there are multiple instruments for multiple structural shocks. Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model's parameters, but, under set-identification, a component of the prior is never revised. We extend the robust Bayesian approach to inference in set-identified models proposed by Giacomini and Kitagawa (2018) -- which allows researchers to relax potentially controversial point-identifying restrictions without having to specify an unrevisable prior -- to proxy SVARs. We provide new results on the frequentist validity of the approach in proxy SVARs. We also explore the effect of instrument strength on inference about the identified set. We illustrate our approach by revisiting Mertens and Ravn (2013) and relaxing the assumption that they impose to obtain point identification.
Book Synopsis Introduction to Bayesian Statistics by : William M. Bolstad
Download or read book Introduction to Bayesian Statistics written by William M. Bolstad and published by John Wiley & Sons. This book was released on 2016-08-23 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: "...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
