Bayesian Multivariate Time Series Methods For Empirical Macroeconomics
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Bayesian Multivariate Time Series Methods For Empirical Macroeconomics
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Author : Gary Koop
language : en
Publisher: Now Publishers Inc
Release Date : 2010
Bayesian Multivariate Time Series Methods For Empirical Macroeconomics written by Gary Koop and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
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.
The Oxford Handbook Of Bayesian Econometrics
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Author : John Geweke
language : en
Publisher: Oxford University Press
Release Date : 2011-09-29
The Oxford Handbook Of Bayesian Econometrics written by John Geweke and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-29 with Business & Economics categories.
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.
Applied Bayesian Modelling
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Author : Peter Congdon
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-23
Applied Bayesian Modelling written by Peter Congdon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-23 with Mathematics categories.
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.
Current Trends In Bayesian Methodology With Applications
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Author : Satyanshu K. Upadhyay
language : en
Publisher: CRC Press
Release Date : 2015-05-21
Current Trends In Bayesian Methodology With Applications written by Satyanshu K. Upadhyay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-21 with Mathematics categories.
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples. This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.
Forecasting Models An Overview With The Help Of R Software
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Author : Editor IJSMI
language : en
Publisher: international Journal of Statistics and Medical Informatics
Release Date : 2019-07-20
Forecasting Models An Overview With The Help Of R Software written by Editor IJSMI and has been published by international Journal of Statistics and Medical Informatics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-20 with Mathematics categories.
Forecasting models – an overview with the help of R software Preface Forecasting models involves predicting the future values of a particular series of data which is mainly based on the time domain. Forecasting models are widely used in the fields such as financial markets, demand for a product and disease outbreak. The objective of the forecasting model is to reduce the error in the forecasting. Most of the Forecasting models are based on time series, a statistical concept which involves Moving Averages, Auto Regressive Integrated Moving Averages (ARIMA), Exponential smoothing and Generalized Auto Regressive Conditional Heteroscedastic (GARCH) Models. Forecasting models which we deal in this book will be explorative forecasting models which take into account the past data to predict the future values. Current day forecasting models uses advanced techniques such as Machine Learning and Deep Learning Algorithms which are more robust and can handle high volume of data. This book starts with the overview of forecasting and time series concepts and moves on to build forecasting models using different time series models. Examples related to forecasting models which are built based on Machine learning also covered. The book uses R statistical software package, an open source statistical package to build the forecasting models. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php https://www.amazon.co.uk/dp/B07VFY53B1
Bayesian Inference In The Social Sciences
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Author : Ivan Jeliazkov
language : en
Publisher: John Wiley & Sons
Release Date : 2014-11-04
Bayesian Inference In The Social Sciences written by Ivan Jeliazkov and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-04 with Mathematics categories.
Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Macro Econometric Analysis On Determinants Of Fertility Behavior
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Author : Hisakazu Kato
language : en
Publisher: Springer Nature
Release Date : 2021-08-06
Macro Econometric Analysis On Determinants Of Fertility Behavior written by Hisakazu Kato and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-06 with Social Science categories.
The book comprises three chapters, with each chapter assigned various type data such as time series data, cross sectional data and panel data. The purpose of this book is to explore the economic and social determinant factors of fertility. Unlike many previous empirical analyses of fertility and the related demographic events, this research has three characteristics. The first is that the relationship between fertility and labor participation by females is thoroughly considered, with much discussion about the structural change between those factors. The second is that time series analysis such as the Bayesian vector autoregressive (BVAR) model or co-integration concepts is applied to explore the determinant factors of fertility. The third is that the effectiveness of public policies related to improve fertility is confirmed. In recent years, micro-econometric analysis has become popular; however, this book takes another approach from the perspective of macro- or semi-macro-econometrics.
Structural Vector Autoregressive Analysis
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Author : Lutz Kilian
language : en
Publisher: Cambridge University Press
Release Date : 2017-11-23
Structural Vector Autoregressive Analysis written by Lutz Kilian and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-23 with Business & Economics categories.
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
State Space Models
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Author : Yong Zeng
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-15
State Space Models written by Yong Zeng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-15 with Business & Economics categories.
State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.
How External Factors Affect Domestic Economy
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Author : Mr.Serhat Solmaz
language : en
Publisher: International Monetary Fund
Release Date : 2015-12-23
How External Factors Affect Domestic Economy written by Mr.Serhat Solmaz and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-23 with Business & Economics categories.
External headwinds, together with domestic vulnerabilities, have loomed over the prospects of emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR consisting of two blocks representing home and foreign factors, which is particularly useful for small open economies. By exploiting the mixed-frequency nature of the model, we show how the toolbox can be used for “nowcasting” the output growth. The conditional forecast results illustrate that regular updates of external information, as well as domestic leading indicators, would significantly enhance the accuracy of forecasts. Moreover, the analysis of variance decompositions shows that external shocks are important drivers of the domestic business cycle.