Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Bayesian Analysis In Economic Theory And Time Series Analysis PDF full book. Access full book title Bayesian Analysis In Economic Theory And Time Series Analysis.
| Author | : Charles A. Holt |
| Publisher | : North-Holland |
| Total Pages | : 200 |
| Release | : 1980 |
| Genre | : Business & Economics |
| ISBN | : |
Bidding for contract; A bayesian approach to the spectral analysis of stationary time series.
| Author | : Richard Michael Cyert |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 278 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 9400931638 |
We began this research with the objective of applying Bayesian methods of analysis to various aspects of economic theory. We were attracted to the Bayesian approach because it seemed the best analytic framework available for dealing with decision making under uncertainty, and the research presented in this book has only served to strengthen our belief in the appropriateness and usefulness of this methodology. More specif ically, we believe that the concept of organizational learning is funda mental to decision making under uncertainty in economics and that the Bayesian framework is the most appropriate for developing that concept. The central and unifying theme of this book is decision making under uncertainty in microeconomic theory. Our fundamental aim is to explore the ways in which firms and households make decisions and to develop models that have a strong empirical connection. Thus, we have attempted to contribute to economic theory by formalizing models of the actual pro cess of decision making under uncertainty. Bayesian methodology pro vides the appropriate vehicle for this formalization.
| Author | : William A. Barnett |
| Publisher | : Cambridge University Press |
| Total Pages | : 248 |
| Release | : 2000-05-22 |
| Genre | : Business & Economics |
| ISBN | : 9780521594240 |
This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
| Author | : John Geweke |
| Publisher | : John Wiley & Sons |
| Total Pages | : 322 |
| Release | : 2005-10-03 |
| Genre | : Mathematics |
| ISBN | : 0471744727 |
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.
| Author | : Mike West |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 720 |
| Release | : 2013-06-29 |
| Genre | : Mathematics |
| ISBN | : 1475793650 |
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
| Author | : Arnold Zellner |
| Publisher | : Cambridge University Press |
| Total Pages | : 186 |
| Release | : 2004-02-19 |
| Genre | : Business & Economics |
| ISBN | : 9780521540445 |
Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide academic and professional appeal.
| Author | : Gary Koop |
| Publisher | : Now Publishers Inc |
| Total Pages | : 104 |
| Release | : 2010 |
| Genre | : Business & Economics |
| ISBN | : 160198362X |
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.
| Author | : John Eatwell |
| Publisher | : Springer |
| Total Pages | : 336 |
| Release | : 1990-07-23 |
| Genre | : Business & Economics |
| ISBN | : 1349208655 |
This is an excerpt from the 4-volume dictionary of economics, a reference book which aims to define the subject of economics today. 1300 subject entries in the complete work cover the broad themes of economic theory. This extract concentrates on time series and statistics.
| Author | : Andy Pole |
| Publisher | : CRC Press |
| Total Pages | : 432 |
| Release | : 2018-10-08 |
| Genre | : Business & Economics |
| ISBN | : 1482267438 |
Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.
| Author | : Lyle D. Broemeling |
| Publisher | : CRC Press |
| Total Pages | : 280 |
| Release | : 2019-04-16 |
| Genre | : Mathematics |
| ISBN | : 0429948921 |
In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.
