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Author : Michael P. Clements
Publisher : OUP USA
Page : 732 pages
File Size : 10,27 MB
Release : 2011-07-08
Category : Business & Economics
ISBN : 0195398645
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.
Author : Siem Jan Koopman
Publisher : Emerald Group Publishing
Page : 685 pages
File Size : 48,91 MB
Release : 2016-01-08
Category : Business & Economics
ISBN : 1785603523
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Author : Jörg Breitung
Publisher :
Page : 40 pages
File Size : 47,62 MB
Release : 2016
Category :
ISBN :
Factor models can cope with many variables without running into scarce degrees of freedom.
Author : Marc Hallin
Publisher : World Scientific Publishing Company
Page : 764 pages
File Size : 47,87 MB
Release : 2020-03-30
Category : Business & Economics
ISBN : 9789813278004
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.
Author : Olaf Hübler
Publisher : Springer Science & Business Media
Page : 236 pages
File Size : 47,11 MB
Release : 2007-04-29
Category : Business & Economics
ISBN : 3540326936
In this book leading German econometricians in different fields present survey articles of the most important new methods in econometrics. The book gives an overview of the field and it shows progress made in recent years and remaining problems.
Author : Jushan Bai
Publisher : Now Publishers Inc
Page : 90 pages
File Size : 32,8 MB
Release : 2008
Category : Business & Economics
ISBN : 1601981449
Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Author : Jörg Breitung
Publisher :
Page : 29 pages
File Size : 25,75 MB
Release : 2005
Category :
ISBN : 9783865580979
Author : Peter Fuleky
Publisher : Springer Nature
Page : 716 pages
File Size : 28,78 MB
Release : 2019-11-28
Category : Business & Economics
ISBN : 3030311503
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.
Author : Francis X. Diebold
Publisher : Oxford University Press
Page : 285 pages
File Size : 33,39 MB
Release : 2015-02-03
Category : Business & Economics
ISBN : 0199338329
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.
Author : Giovanni Petris
Publisher : Springer Science & Business Media
Page : 258 pages
File Size : 48,44 MB
Release : 2009-06-12
Category : Mathematics
ISBN : 0387772383
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.