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| an introduction to modern bayesian econometrics: Introduction to Modern Bayesian Econometrics Tony Lancaster, 2004-06-18 In this new and expanding area, Tony Lancaster’s text is the first comprehensive introduction to the Bayesian way of doing applied economics. Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method; Emphasizes computation and the study of probability distributions by computer sampling; Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data; Details causal inference and inference about structural econometric models; Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software Supported by online supplements, including Data Sets and Solutions to Problems, at www.blackwellpublishing.com/lancaster |
| an introduction to modern bayesian econometrics: Introduction to Modern Bayesian Econometrics Tony Lancaster, 2004-06-28 Almost two hundred and forty years ago, an English clergyman named Thomas Bayes developed a method to calculate the chances of uncertain events. While his method has extensive applications to the work of applied economists, it is only recent advances in computing that have made it possible to exploit the full power of the Bayesian way of doing applied economics.In this new and expanding area, Tony Lancasters text provides a comprehensive introduction to the Bayesian way of doing applied economics. Using clear explanations and practical illustrations and problems, the text presents innovative, computer-intensive ways for applied economists to use the Bayesian method.The Introduction emphasizes computation and the study of probability distributions by computer sampling, showing how these techniques can provide exact inferences about a wide range of econometric problems. Covering all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data, it also details causal inference and inference about structural econometric models. In addition, each chapter includes numerical and graphical examples and demonstrates their solutions using the S programming language and Bugs software. |
| an introduction to modern bayesian econometrics: Introduction to Bayesian Econometrics Edward Greenberg, 2013 This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language. |
| an introduction to modern bayesian econometrics: Contemporary Bayesian Econometrics and Statistics John Geweke, 2005-10-03 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. |
| an introduction to modern bayesian econometrics: Bayesian Econometric Methods Joshua Chan, Gary Koop, Dale J. Poirier, Justin L. Tobias, 2019-08-15 Illustrates Bayesian theory and application through a series of exercises in question and answer format. |
| an introduction to modern bayesian econometrics: The Oxford Handbook of Bayesian Econometrics John Geweke, Gary Koop, Herman van Dijk, 2011-09-29 Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions 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 chapters 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 chapters on Bayesian principles and methodology. |
| an introduction to modern bayesian econometrics: Bayesian Econometrics Gary Koop, 2003 Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics. |
| an introduction to modern bayesian econometrics: Complete and Incomplete Econometric Models John Geweke, 2010-02-08 Econometric models are widely used in the creation and evaluation of economic policy in the public and private sectors. But these models are useful only if they adequately account for the phenomena in question, and they can be quite misleading if they do not. In response, econometricians have developed tests and other checks for model adequacy. All of these methods, however, take as given the specification of the model to be tested. In this book, John Geweke addresses the critical earlier stage of model development, the point at which potential models are inherently incomplete. Summarizing and extending recent advances in Bayesian econometrics, Geweke shows how simple modern simulation methods can complement the creative process of model formulation. These methods, which are accessible to economics PhD students as well as to practicing applied econometricians, streamline the processes of model development and specification checking. Complete with illustrations from a wide variety of applications, this is an important contribution to econometrics that will interest economists and PhD students alike. |
| an introduction to modern bayesian econometrics: Intermediate Statistics and Econometrics Dale J. Poirier, 1995 The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses-giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills. The introduction closes with an example of a real-world data set-the Challengerspace shuttle disaster-that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables. |
| an introduction to modern bayesian econometrics: Bayesian Econometrics Mauro Bernardi, Stefano Grassi, Francesco Ravazzolo, 2020-12-28 Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis. |
| an introduction to modern bayesian econometrics: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 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. |
| an introduction to modern bayesian econometrics: A Guide to Econometrics Peter Kennedy, 2008-02-19 Dieses etwas andere Lehrbuch bietet keine vorgefertigten Rezepte und Problemlösungen, sondern eine kritische Diskussion ökonometrischer Modelle und Methoden: voller überraschender Fragen, skeptisch, humorvoll und anwendungsorientiert. Sein Erfolg gibt ihm Recht. |
| an introduction to modern bayesian econometrics: Bayesian and Frequentist Regression Methods Jon Wakefield, 2013-01-04 Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. |
| an introduction to modern bayesian econometrics: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics Gary Koop, Dimitris Korobilis, 2010 Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used. |
| an introduction to modern bayesian econometrics: An Introduction to Probabilistic Modeling Pierre Bremaud, 2012-12-06 Introduction to the basic concepts of probability theory: independence, expectation, convergence in law and almost-sure convergence. Short expositions of more advanced topics such as Markov Chains, Stochastic Processes, Bayesian Decision Theory and Information Theory. |
| an introduction to modern bayesian econometrics: A First Course in Bayesian Statistical Methods Peter D. Hoff, 2009-06-02 A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run as-is allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods. |
| an introduction to modern bayesian econometrics: Computational Bayesian Statistics M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller, 2019-02-28 This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods. |
| an introduction to modern bayesian econometrics: Bayesian Nonparametrics J.K. Ghosh, R.V. Ramamoorthi, 2006-05-11 This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics. |
| an introduction to modern bayesian econometrics: Spatial Econometrics Harry Kelejian, Gianfranco Piras, 2017-07-20 Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentation with unusual depth of coverage. Introducing and formalizing the principles of, and 'need' for, models which define spatial interactions, the book provides a comprehensive framework for almost every major facet of modern science. Subjects covered at length include spatial regression models, weighting matrices, estimation procedures and the complications associated with their use. The work particularly focuses on models of uncertainty and estimation under various complications relating to model specifications, data problems, tests of hypotheses, along with systems and panel data extensions which are covered in exhaustive detail. Extensions discussing pre-test procedures and Bayesian methodologies are provided at length. Throughout, direct applications of spatial models are described in detail, with copious illustrative empirical examples demonstrating how readers might implement spatial analysis in research projects. Designed as a textbook and reference companion, every chapter concludes with a set of questions for formal or self--study. Finally, the book includes extensive supplementing information in a large sample theory in the R programming language that supports early career econometricians interested in the implementation of statistical procedures covered. - Combines advanced theoretical foundations with cutting-edge computational developments in R - Builds from solid foundations, to more sophisticated extensions that are intended to jumpstart research careers in spatial econometrics - Written by two of the most accomplished and extensively published econometricians working in the discipline - Describes fundamental principles intuitively, but without sacrificing rigor - Provides empirical illustrations for many spatial methods across diverse field - Emphasizes a modern treatment of the field using the generalized method of moments (GMM) approach - Explores sophisticated modern research methodologies, including pre-test procedures and Bayesian data analysis |
| an introduction to modern bayesian econometrics: Introduction to the Mathematical and Statistical Foundations of Econometrics Herman J. Bierens, 2004-12-20 This book is intended for use in a rigorous introductory PhD level course in econometrics. |
| an introduction to modern bayesian econometrics: Applied Econometrics with R Christian Kleiber, Achim Zeileis, 2008-12-10 R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research. |
| an introduction to modern bayesian econometrics: Bayesian Computation with R Jim Albert, 2009-04-20 There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). |
| an introduction to modern bayesian econometrics: A Student’s Guide to Bayesian Statistics Ben Lambert, 2018-04-20 Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. |
| an introduction to modern bayesian econometrics: The Econometric Analysis of Transition Data Tony Lancaster, 1990 This book presents statistical methods for analysis of the duration of events. The primary focus is on models for single-spell data, events in which individual agents are observed for a single duration. Some attention is also given to multiple-spell data. The first part of the book covers model specification, including both structural and reduced form models and models with and without neglected heterogeneity. The book next deals with likelihood based inference about such models, with sections on full and semiparametric specification. A final section treats graphical and numerical methods of specification testing. This is the first published exposition of current econometric methods for the study of duration data. |
| an introduction to modern bayesian econometrics: Introduction to Modern Time Series Analysis Gebhard Kirchgässner, Jürgen Wolters, 2008-08-27 This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary. |
| an introduction to modern bayesian econometrics: Econometrics in Theory and Practice Panchanan Das, 2019-09-05 This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research. The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research. Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata 15.1, and assumes that the reader is somewhat familiar with the Strata software. The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis that economists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data. There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data. In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. It covers intensively both the univariate and multivariate time series econometric models and their applications with software programming in six chapters. Part IV takes care of panel data analysis in four chapters. Different aspects of fixed effects and random effects are discussed here. Panel data analysis has been extended by taking dynamic panel data models which are most suitable for macroeconomic research. The book is invaluable for students and researchers of social sciences, business, management, operations research, engineering, and applied mathematics. |
| an introduction to modern bayesian econometrics: Econometric Theory and Methods Russell Davidson, 2009-04-30 Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. |
| an introduction to modern bayesian econometrics: Structural Macroeconometrics David N. DeJong, Chetan Dave, 2011-10-03 The revised edition of the essential resource on macroeconometrics Structural Macroeconometrics provides a thorough overview and in-depth exploration of methodologies, models, and techniques used to analyze forces shaping national economies. In this thoroughly revised second edition, David DeJong and Chetan Dave emphasize time series econometrics and unite theoretical and empirical research, while taking into account important new advances in the field. The authors detail strategies for solving dynamic structural models and present the full range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The authors look at recent strides that have been made to enhance numerical efficiency, consider the expanded applicability of dynamic factor models, and examine the use of alternative assumptions involving learning and rational inattention on the part of decision makers. The treatment of methodologies for obtaining nonlinear model representations has been expanded, and linear and nonlinear model representations are integrated throughout the text. The book offers a rich array of implementation algorithms, sample empirical applications, and supporting computer code. Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals. |
| an introduction to modern bayesian econometrics: Bayesian Time Series Models David Barber, A. Taylan Cemgil, Silvia Chiappa, 2011-08-11 The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science. |
| an introduction to modern bayesian econometrics: Panel Data Econometrics Mike Tsionas, 2019-06-19 Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts |
| an introduction to modern bayesian econometrics: Bayesian Data Analysis for the Behavioral and Neural Sciences Todd E. Hudson, 2021-06-30 This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond frequentist concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called hypothesis testing) problems most frequently encountered in real-world applications. |
| an introduction to modern bayesian econometrics: Doing Meta-Analysis with R Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert, 2021-09-15 Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book |
| an introduction to modern bayesian econometrics: Bayesian Analysis with Stata John Thompson, 2014-05-06 Bayesian Analysis with Stata is a compendium of Stata user-written commands for Bayesian analysis. |
| an introduction to modern bayesian econometrics: Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman, Jennifer Hill, 2007 This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. |
| an introduction to modern bayesian econometrics: Probability and Statistics for Economists Bruce Hansen, 2022-06-28 A comprehensive and up-to-date introduction to the mathematics that all economics students need to know Probability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for Economists provides graduate and PhD students with an essential introduction to mathematical probability and statistical theory, which are the basis of the methods used in econometrics. This incisive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of the mathematics that every economist needs to know. Covers probability and statistics with mathematical rigor while emphasizing intuitive explanations that are accessible to economics students of all backgrounds Discusses random variables, parametric and multivariate distributions, sampling, the law of large numbers, central limit theory, maximum likelihood estimation, numerical optimization, hypothesis testing, and more Features hundreds of exercises that enable students to learn by doing Includes an in-depth appendix summarizing important mathematical results as well as a wealth of real-world examples Can serve as a core textbook for a first-semester PhD course in econometrics and as a companion book to Bruce E. Hansen’s Econometrics Also an invaluable reference for researchers and practitioners |
| an introduction to modern bayesian econometrics: Bayesian Estimation of DSGE Models Edward P. Herbst, Frank Schorfheide, 2015-12-29 Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions. |
| an introduction to modern bayesian econometrics: Econometrics Bruce Hansen, 2022-08-16 The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgroundsDraws on integrated, research-level datasets, provided on an accompanying websiteDiscusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learningFeatures hundreds of exercises that enable students to learn by doingIncludes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examplesCan serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists |
| an introduction to modern bayesian econometrics: Doing Bayesian Data Analysis John Kruschke, 2010-11-25 There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS software - Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). - Coverage of experiment planning - R and BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment |
| an introduction to modern bayesian econometrics: Bayesian Statistics in Actuarial Science Stuart A. Klugman, 2013-04-17 The debate between the proponents of classical and Bayesian statistica} methods continues unabated. It is not the purpose of the text to resolve those issues but rather to demonstrate that within the realm of actuarial science there are a number of problems that are particularly suited for Bayesian analysis. This has been apparent to actuaries for a long time, but the lack of adequate computing power and appropriate algorithms had led to the use of various approximations. The two greatest advantages to the actuary of the Bayesian approach are that the method is independent of the model and that interval estimates are as easy to obtain as point estimates. The former attribute means that once one learns how to analyze one problem, the solution to similar, but more complex, problems will be no more difficult. The second one takes on added significance as the actuary of today is expected to provide evidence concerning the quality of any estimates. While the examples are all actuarial in nature, the methods discussed are applicable to any structured estimation problem. In particular, statisticians will recognize that the basic credibility problem has the same setting as the random effects model from analysis of variance. |
| an introduction to modern bayesian econometrics: Analysis of Economic Data Gary Koop, 2013-09-23 Analysis of Economic Data has, over three editions, become firmly established as a successful textbook for students studying data analysis whose primary interest is not in econometrics, statistics or mathematics. It introduces students to basic econometric techniques and shows the reader how to apply these techniques in the context of real-world empirical problems. The book adopts a largely non-mathematical approach relying on verbal and graphical inuition and covers most of the tools used in modern econometrics research. It contains extensive use of real data examples and involves readers in hands-on computer work. |
An Introduction to Modern Bayesian Econometrics - Brown …
This book is about the Bayesian approach to inference; it is not a book about comparative methods and it contains little about traditional approaches which are covered in many …
An Introduction To Modern Bayesian Econometrics
Bayesian econometrics offers a powerful alternative to frequentist approaches, leveraging prior knowledge and incorporating uncertainty more explicitly. This guide provides a comprehensive …
An Introduction To Modern Bayesian Econometrics (book)
An Introduction to Modern Bayesian Econometrics Description: This paper provides an accessible introduction to the burgeoning field of modern Bayesian econometrics. We delve into the …
An Introduction To Modern Bayesian Econometrics - Johns …
8 Feb 2024 · Introduction to Modern Bayesian Econometrics - Wiley • Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and …
An Introduction To Modern Bayesian Econometrics
describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can …
BAYESIAN ECONOMETRICS - MIT
Bayesian econometrics employs Bayesian methods for inference about economic questions using economic data. In the following, we brie°y review these methods and their applications. …
Introduction to Bayesian Econometrics - Cambridge University …
Introduction to Bayesian Econometrics This concise textbook is an introduction to econometrics from the Bayesian view-point. It begins with an explanation of the basic ideas of subjective …
Introduction to Modern Bayesian Econometrics - Wiley
• Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and the study of probability distributions by computer sampling • …
An Introduction To Modern Bayesian Econometrics - unap.edu.pe
Introduction to Modern Bayesian Econometrics - Wiley • Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and the study of …
Introduction to Modern Bayesian Econometrics (Tony Lancaster)
7 Mar 2014 · The author uses several S code fragments to simulate data and explain Bayesian techniques. The core of the book is given in Chapter 1, Chapter 2 and Chapter 4. Chapter 1 …
Second Edition - Cambridge University Press & Assessment
Introduction to Bayesian Econometrics Second Edition This textbook, now in its second edition, is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of …
Machine Learning Econometrics: Bayesian algorithms and …
This Chapter provides a review of popular estimation algorithms for Bayesian inference in econometrics and surveys alternative algorithms developed in machine learning and …
An Introduction To Modern Bayesian Econometrics
describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can …
Modern Bayesian Econometrics - Hedibert
Modern Bayesian Econometrics By Hedibert Freitas Lopes Professor of Statistics and Econometrics INSPER Institute of Education and Research PART 1: Modeling and …
MODERN BAYESIAN ECONOMETRICS LECTURES BY TONY LANCASTER …
MODERN BAYESIAN ECONOMETRICS LECTURES BY TONY LANCASTER January 2006 AN OVERVIEW These lectures are based on my book An Introduction to Modern Bayesian …
Bayesian and Classical Approaches to Inference and Model …
The course provides an introduction to Bayesian inference from the perspectives of a classically trained econometrician. Beginning with Bayes Theorem applied to random parameters, the …
An Introduction to Modern Bayesian Econometrics
An Introduction to Modern Bayesian Econometrics. Answers to Selected Exercises. Most of the exercises in the book ask readers to simulate and calculate. So there are really no answers to …
An Introduction To Modern Bayesian Econometrics
Bayesian econometrics offers a powerful alternative to frequentist approaches, leveraging prior knowledge and incorporating uncertainty more explicitly. This guide provides a comprehensive …
A GUIDE TO Bayesian Econometrics for Modern Marketers
A Guide to Bayesian Econometrics What is Bayesian? Bayesian refers to methods in logic and statistics named after the English mathematician and clergyman Thomas Bayes (c. 1702–61), …
Notes on Econometrics I - Scholars at Harvard
This is what we will learn to be called a Bayesian approach. hypothesis test - we can use our data to see if we can reject various hypothesis about our data (for example, a hypothesis may be …
An Introduction to Modern Bayesian Econometrics - Brown …
This book is about the Bayesian approach to inference; it is not a book about comparative methods and it contains little about traditional approaches which are covered in many textbooks. My aim has been to answer two rather simple questions. The first is “What is Bayesian Econometrics?” and the second is “How do I do it?”
An Introduction To Modern Bayesian Econometrics
Bayesian econometrics offers a powerful alternative to frequentist approaches, leveraging prior knowledge and incorporating uncertainty more explicitly. This guide provides a comprehensive introduction, covering key concepts, practical applications, and potential pitfalls. I. Core Concepts: Understanding the Bayesian Framework.
An Introduction To Modern Bayesian Econometrics (book)
An Introduction to Modern Bayesian Econometrics Description: This paper provides an accessible introduction to the burgeoning field of modern Bayesian econometrics. We delve into the foundations of Bayesian inference, exploring its key concepts and demonstrating its application to a range of economic models.
An Introduction To Modern Bayesian Econometrics - Johns …
8 Feb 2024 · Introduction to Modern Bayesian Econometrics - Wiley • Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and the study of probability distributions by computer sampling • Includes numerical and graphical examples in each chapter, demonstrating
An Introduction To Modern Bayesian Econometrics
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
BAYESIAN ECONOMETRICS - MIT
Bayesian econometrics employs Bayesian methods for inference about economic questions using economic data. In the following, we brie°y review these methods and their applications. Suppose a data vector X = (X1; :::; Xn) follows a distribution with a density func-tion pn(xjμ) which is fully characterized by some parameter vector μ = (μ1 ...
Introduction to Bayesian Econometrics - Cambridge University …
Introduction to Bayesian Econometrics This concise textbook is an introduction to econometrics from the Bayesian view-point. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency.
Introduction to Modern Bayesian Econometrics - Wiley
• Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and the study of probability distributions by computer sampling • Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language
An Introduction To Modern Bayesian Econometrics - unap.edu.pe
Introduction to Modern Bayesian Econometrics - Wiley • Provides a comprehensive introduction to the Bayesian way of doing applied economics • Emphasizes computation and the study of probability distributions by computer sampling • Includes numerical and graphical examples in each chapter, demonstrating their
Introduction to Modern Bayesian Econometrics (Tony Lancaster)
7 Mar 2014 · The author uses several S code fragments to simulate data and explain Bayesian techniques. The core of the book is given in Chapter 1, Chapter 2 and Chapter 4. Chapter 1 talks about Bayesian algorithm. Chapter 2 on prediction and model criticism and Chapter 4 …
Second Edition - Cambridge University Press & Assessment
Introduction to Bayesian Econometrics Second Edition This textbook, now in its second edition, is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjec-tive probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency.
Machine Learning Econometrics: Bayesian algorithms and …
This Chapter provides a review of popular estimation algorithms for Bayesian inference in econometrics and surveys alternative algorithms developed in machine learning and computing science that allow for efficient computation in high-dimensional settings.
An Introduction To Modern Bayesian Econometrics
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
Modern Bayesian Econometrics - Hedibert
Modern Bayesian Econometrics By Hedibert Freitas Lopes Professor of Statistics and Econometrics INSPER Institute of Education and Research PART 1: Modeling and computation (6h) 1. Likelihood function & prior, predictive and posterior densities 2. Model selection/averaging, posterior predictives, Bayes factors and model probabilities 3.
MODERN BAYESIAN ECONOMETRICS LECTURES BY TONY LANCASTER …
MODERN BAYESIAN ECONOMETRICS LECTURES BY TONY LANCASTER January 2006 AN OVERVIEW These lectures are based on my book An Introduction to Modern Bayesian Econometrics , Blackwells, May 2004 and some more recent material. The main software used is WinBUGS http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml This is shareware.
Bayesian and Classical Approaches to Inference and Model …
The course provides an introduction to Bayesian inference from the perspectives of a classically trained econometrician. Beginning with Bayes Theorem applied to random parameters, the material examines a number of key issues for classical estimation, and where appropriate considers the Bayesian analog. The material moves from the fundamen-tal ...
An Introduction to Modern Bayesian Econometrics
An Introduction to Modern Bayesian Econometrics. Answers to Selected Exercises. Most of the exercises in the book ask readers to simulate and calculate. So there are really no answers to give. This is consistent with the spirit in which the book was written which empasized calculation and not formal, and often rather tedious, algebraic derivations.
An Introduction To Modern Bayesian Econometrics
Bayesian econometrics offers a powerful alternative to frequentist approaches, leveraging prior knowledge and incorporating uncertainty more explicitly. This guide provides a comprehensive introduction, covering key concepts, practical applications,
A GUIDE TO Bayesian Econometrics for Modern Marketers
A Guide to Bayesian Econometrics What is Bayesian? Bayesian refers to methods in logic and statistics named after the English mathematician and clergyman Thomas Bayes (c. 1702–61), in particular methods related to probability inference: in other words, using the knowledge of prior events to predict future events.
Notes on Econometrics I - Scholars at Harvard
This is what we will learn to be called a Bayesian approach. hypothesis test - we can use our data to see if we can reject various hypothesis about our data (for example, a hypothesis may be that the mean of a distribution is 7 or that education has no effect on income) estimator - our “best guess” of what the population param-
