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Book Bayesian Nonparametric Vector Autoregressive Models

Download or read book Bayesian Nonparametric Vector Autoregressive Models written by Maria Kalli and published by . This book was released on 2017 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vector autoregressive (VAR) models are the main work-horse model for macroeconomic forecasting, and provide a framework for the analysis of complex dynamics that are present between macroeconomic variables. Whether a classical or a Bayesian approach is adopted, most VAR models are linear with Gaussian innovations. This can limit the model's ability to explain the relationships in macroeconomic series. We propose a nonparametric VAR model that allows for nonlinearity in the conditional mean, heteroscedasticity in the conditional variance, and non-Gaussian innovations. Our approach differs to that of previous studies by modelling the stationary and transition densities using Bayesian nonparametric methods. Our Bayesian nonparametric VAR (BayesNP-VAR) model is applied to US and UK macroeconomic time series, and compared to other Bayesian VAR models. We show that BayesNP-VAR is a flexible model that is able to account for nonlinear relationships as well as heteroscedas- ticity in the data. In terms of short-run out-of-sample forecasts, we show that BayesNP-VAR predictively outperforms competing models.

Book Bayesian Nonparametric Autoregressive Models with Applications

Download or read book Bayesian Nonparametric Autoregressive Models with Applications written by Maria Anna Di Lucca and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Mathematical and Statistical Methods for Actuarial Sciences and Finance

Download or read book Mathematical and Statistical Methods for Actuarial Sciences and Finance written by Marco Corazza and published by Springer. This book was released on 2018-07-17 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.

Book Stationary Autoregressive Models Via a Bayesian Nonparametric Approach

Download or read book Stationary Autoregressive Models Via a Bayesian Nonparametric Approach written by Ramsés H. Mena and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An approach to constructing strictly stationary AR(1)-type models with arbitrary stationary distributions and a flexible dependence structure is introduced. Bayesian nonparametric predictive density functions, based on single observations, are used to construct the one-step ahead predictive density. This is a natural and highly flexible way to model a one-step predictive/transition density.

Book Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model  SUR

Download or read book Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model SUR written by Monica Billio and published by . This book was released on 2017 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To avoid overparametrization and overfitting issues, we propose a hierarchical Dirichlet process prior for SUR models, which allows shrinkage of SUR coefficients toward multiple locations and identi cation of group of coefficients. We propose a two-stage hierarchical prior distribution, where the first stage of the hierarchy consists in a Lasso conditionally independent prior distribution of the Normal-Gamma family for the SUR coefficients. The second stage is given by a random mixture distribution for the Normal-Gamma hyperparameters, which allows for parameter parsimony through two components: the first one is a random Dirac point-mass distribution, which induces sparsity in the SUR coefficients; the second is a Dirichlet process prior, which allows for clustering of the SUR coefficients.Our sparse SUR model with multiple locations, scales and shapes includes the Vector autoregressive models (VAR) and dynamic panel models as special cases. We consider an international business cycle applications to show the effectiveness of our model and inference approach. Our new multiple shrinkage prior model allows us to better understand shock transmission phenomena, to extract coloured networks and to classify the linkages strenght. The empirical results represent a different point of view on international business cycles providing interesting new findings in the relationship between core and pheriphery countries.

Book Bayesian Nonparametric Data Analysis

Download or read book Bayesian Nonparametric Data Analysis written by Peter Müller and published by Springer. This book was released on 2015-06-17 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

Book Var Models in Macroeconomics   New Developments and Applications

Download or read book Var Models in Macroeconomics New Developments and Applications written by Thomas B. Fomby and published by Emerald Group Publishing Limited. This book was released on 2013-12-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Econometrics publishes original scholarly econometric papers with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics, throughout the empirical economic, business and social science literature.

Book Matrix Differential Calculus with Applications in Statistics and Econometrics

Download or read book Matrix Differential Calculus with Applications in Statistics and Econometrics written by Jan R. Magnus and published by John Wiley & Sons. This book was released on 2019-03-15 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it. Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference. Fulfills the need for an updated and unified treatment of matrix differential calculus Contains many new examples and exercises based on questions asked of the author over the years Covers new developments in field and features new applications Written by a leading expert and pioneer of the theory Part of the Wiley Series in Probability and Statistics Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.

Book Bayesian Analysis of Cointegrated Vector Autoregressive Models

Download or read book Bayesian Analysis of Cointegrated Vector Autoregressive Models written by and published by . This book was released on 2004 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Practical Nonparametric and Semiparametric Bayesian Statistics

Download or read book Practical Nonparametric and Semiparametric Bayesian Statistics written by Dipak D. Dey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.

Book Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Download or read book Adaptive Shrinkage in Bayesian Vector Autoregressive Models written by Martin Feldkircher and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Bayesian Nonparametrics

    Book Details:
  • Author : Nils Lid Hjort
  • Publisher : Cambridge University Press
  • Release : 2010-04-12
  • ISBN : 1139484605
  • Pages : 309 pages

Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.