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The Generalised Dynamic Factor Model
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Book Synopsis The Oxford Handbook of Economic Forecasting by : Michael P. Clements
Download or read book The Oxford Handbook of Economic Forecasting written by Michael P. Clements and published by OUP USA. This book was released on 2011-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Book Synopsis The Generalised Dynamic Factor Model by :
Download or read book The Generalised Dynamic Factor Model written by and published by . This book was released on 1999 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial and Macroeconomic Connectedness by : Francis X. Diebold
Download or read book Financial and Macroeconomic Connectedness written by Francis X. Diebold and published by Oxford University Press. This book was released on 2015-02-03 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.
Book Synopsis The Generalized Dynamic Factor Model by :
Download or read book The Generalized Dynamic Factor Model written by and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2011-06-24 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Book Synopsis Statistical Learning for Big Dependent Data by : Daniel Peña
Download or read book Statistical Learning for Big Dependent Data written by Daniel Peña and published by John Wiley & Sons. This book was released on 2021-05-04 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.
Book Synopsis Dynamic Factor Models by : Jörg Breitung
Download or read book Dynamic Factor Models written by Jörg Breitung and published by . This book was released on 2016 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor models can cope with many variables without running into scarce degrees of freedom.
Book Synopsis The Generalized Dynamic Factor Model by : Mario Forni
Download or read book The Generalized Dynamic Factor Model written by Mario Forni and published by . This book was released on 1998 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Time Series in High Dimension: the General Dynamic Factor Model by : Marc Hallin
Download or read book Time Series in High Dimension: the General Dynamic Factor Model written by Marc Hallin and published by World Scientific Publishing Company. This book was released on 2020-03-30 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.
Book Synopsis The Generalized Dynamic Factor Model by : Mario Forni
Download or read book The Generalized Dynamic Factor Model written by Mario Forni and published by . This book was released on 2000 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Generalized Dynamic Factor Model: Identification and Estimation by :
Download or read book The Generalized Dynamic Factor Model: Identification and Estimation written by and published by . This book was released on 1999 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Chance and Stability by : Vladimir V. Uchaikin
Download or read book Chance and Stability written by Vladimir V. Uchaikin and published by Walter de Gruyter. This book was released on 2011-09-08 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Book Synopsis The Generalized Dynamic Factor Model by :
Download or read book The Generalized Dynamic Factor Model written by and published by . This book was released on 2002 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neuronal Dynamics by : Wulfram Gerstner
Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Book Synopsis Dynamic Models in Biology by : Stephen P. Ellner
Download or read book Dynamic Models in Biology written by Stephen P. Ellner and published by Princeton University Press. This book was released on 2011-09-19 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Book Synopsis Latent Variable Models and Factor Analysis by : David J. Bartholomew
Download or read book Latent Variable Models and Factor Analysis written by David J. Bartholomew and published by Wiley. This book was released on 1999-08-10 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hitherto latent variable modelling has hovered on the fringes of the statistical mainstream but if the purpose of statistics is to deal with real problems, there is every reason for it to move closer to centre stage. In the social sciences especially, latent variables are common and if they are to be handled in a truly scientific manner, statistical theory must be developed to include them. This book aims to show how that should be done. This second edition is a complete re-working of the book of the same name which appeared in the Griffin’s Statistical Monographs in 1987. Since then there has been a surge of interest in latent variable methods which has necessitated a radical revision of the material but the prime object of the book remains the same. It provides a unified and coherent treatment of the field from a statistical perspective. This is achieved by setting up a sufficiently general framework to enable the derivation of the commonly used models. The subsequent analysis is then done wholly within the realm of probability calculus and the theory of statistical inference. Numerical examples are provided as well as the software to carry them out ( where this is not otherwise available). Additional data sets are provided in some cases so that the reader can aquire a wider experience of analysis and interpretation.
Book Synopsis Macroeconomic Forecasting in the Era of Big Data by : Peter Fuleky
Download or read book Macroeconomic Forecasting in the Era of Big Data written by Peter Fuleky and published by Springer Nature. This book was released on 2019-11-28 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
